Python mxnet.metric.EvalMetric() Examples
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code examples of mxnet.metric.EvalMetric().
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Example #1
| Source File: detection_module.py From simpledet with Apache License 2.0 | 6 votes |
|
def update_metric(self, eval_metric, labels, pre_sliced=False):
"""Evaluates and accumulates evaluation metric on outputs of the last forward computation.
See Also
----------
:meth:`BaseModule.update_metric`.
Parameters
----------
eval_metric : EvalMetric
Evaluation metric to use.
labels : list of NDArray if `pre_sliced` parameter is set to `False`,
list of lists of NDArray otherwise. Typically `data_batch.label`.
pre_sliced: bool
Whether the labels are already sliced per device (default: False).
"""
if mxnet.__version__ >= "1.3.0":
self._exec_group.update_metric(eval_metric, labels, pre_sliced)
else:
self._exec_group.update_metric(eval_metric, labels)
Example #2
| Source File: detection_module.py From groupsoftmax-simpledet with Apache License 2.0 | 6 votes |
|
def update_metric(self, eval_metric, labels, pre_sliced=False):
"""Evaluates and accumulates evaluation metric on outputs of the last forward computation.
See Also
----------
:meth:`BaseModule.update_metric`.
Parameters
----------
eval_metric : EvalMetric
Evaluation metric to use.
labels : list of NDArray if `pre_sliced` parameter is set to `False`,
list of lists of NDArray otherwise. Typically `data_batch.label`.
pre_sliced: bool
Whether the labels are already sliced per device (default: False).
"""
if mxnet.__version__ >= "1.3.0":
self._exec_group.update_metric(eval_metric, labels, pre_sliced)
else:
self._exec_group.update_metric(eval_metric, labels)
Example #3
| Source File: module.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #4
| Source File: module.py From Deep-Feature-Flow-Segmentation with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #5
| Source File: module.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #6
| Source File: module.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #7
| Source File: module.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #8
| Source File: module.py From Sequence-Level-Semantics-Aggregation with Apache License 2.0 | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #9
| Source File: module.py From Faster_RCNN_for_DOTA with Apache License 2.0 | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #10
| Source File: module.py From mx-DeepIM with Apache License 2.0 | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #11
| Source File: module.py From Accel with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #12
| Source File: module.py From Accel with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #13
| Source File: module.py From Accel with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #14
| Source File: module.py From Accel with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #15
| Source File: module.py From Relation-Networks-for-Object-Detection with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #16
| Source File: module.py From RoITransformer_DOTA with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #17
| Source File: module.py From RoITransformer_DOTA with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #18
| Source File: module.py From Deformable-ConvNets with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #19
| Source File: module.py From Deformable-ConvNets with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #20
| Source File: module.py From Deformable-ConvNets with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #21
| Source File: module.py From Deformable-ConvNets with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #22
| Source File: module.py From Flow-Guided-Feature-Aggregation with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #23
| Source File: module.py From Deep-Feature-Flow with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #24
| Source File: module.py From Deep-Feature-Flow with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #25
| Source File: module.py From MANet_for_Video_Object_Detection with Apache License 2.0 | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #26
| Source File: module.py From kaggle-rsna18 with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #27
| Source File: module.py From kaggle-rsna18 with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #28
| Source File: module.py From kaggle-rsna18 with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #29
| Source File: module.py From kaggle-rsna18 with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
Example #30
| Source File: module.py From kaggle-rsna18 with MIT License | 5 votes |
|
def update_metric(self, eval_metric, labels):
"""Evaluate and accumulate evaluation metric on outputs of the last forward computation.
Parameters
----------
eval_metric : EvalMetric
labels : list of NDArray
Typically `data_batch.label`.
"""
self._exec_group.update_metric(eval_metric, labels)
