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## Evaluator Design
### The Problem
During training or serving, we provide the evaluation function to measure the model performance, e.g., accuracy, precision. In the operator based framework design, the data go through the network pipeline batch by batch. As a result, inside the operator, we only can calculate one minibatch metrics. We need to provide a mechanism to calculate the metrics for each N pass/batch the user wanted.
### Evaluator Design
Currently, every operation is expressed in the graph. we divide the evaluator process into three steps.
1. Initialize the metric state and add it into the block.
2. Calculate the statistic of the metric state in every mini-batch. The single operator is only responsible for calculating necessary statistics for one mini-batch. For example, accuracy operator only calculate a minibatch data if run once.
3. Merge the mini-batch statistics to form the evaluation result for multiple mini-batches. When it comes to distributed training/Multi-GPU training, aggregate the value from different devices.
### Implementation
This design is shown in python API.
Each metric operator need to caculate the metric statistic and return the batch aware states, Python side responsible for accumulate the states for each pass.
```python
class Evaluator(object):
"""
Evaluator Base class.
"""
def __init__(self, name, **kwargs):
"""
Different evaluator may has different metric states. E.g, Accuracy need two variables, total and right sample counts.
Auc need four variables, `true_positives`,
`true_negatives`, `false_positives` and `false_negatives`. So every evaluator should create its needed variables and append to main_program
The initialization of Evaluator should be responsible for:
create metric states and append to the main_program
"""
pass
def _update_ops(self, input, label, **kwargs)
"""
Add mini-batch evaluator caculate operators to the main_program.
Add increment operator to accumulate the metric states.
"""
def reset(self, executor, reset_program=None):
"""
Reset metric states at the begin of each pass/user specified batch number.
Execute the reset_program to reset the states.
"""
def eval(self, executor, eval_program=None):
"""
Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
Execute the eval_program and return the result.
"""
return eval_result
```
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<div class="section" id="evaluator-design">
<span id="evaluator-design"></span><h1>Evaluator Design<a class="headerlink" href="#evaluator-design" title="Permalink to this headline"></a></h1>
<div class="section" id="the-problem">
<span id="the-problem"></span><h2>The Problem<a class="headerlink" href="#the-problem" title="Permalink to this headline"></a></h2>
<p>During training or serving, we provide the evaluation function to measure the model performance, e.g., accuracy, precision. In the operator based framework design, the data go through the network pipeline batch by batch. As a result, inside the operator, we only can calculate one minibatch metrics. We need to provide a mechanism to calculate the metrics for each N pass/batch the user wanted.</p>
</div>
<div class="section" id="evaluator-design">
<span id="id1"></span><h2>Evaluator Design<a class="headerlink" href="#evaluator-design" title="Permalink to this headline"></a></h2>
<p>Currently, every operation is expressed in the graph. we divide the evaluator process into three steps.</p>
<ol class="simple">
<li>Initialize the metric state and add it into the block.</li>
<li>Calculate the statistic of the metric state in every mini-batch. The single operator is only responsible for calculating necessary statistics for one mini-batch. For example, accuracy operator only calculate a minibatch data if run once.</li>
</ol>
<ol class="simple">
<li>Merge the mini-batch statistics to form the evaluation result for multiple mini-batches. When it comes to distributed training/Multi-GPU training, aggregate the value from different devices.</li>
</ol>
</div>
<div class="section" id="implementation">
<span id="implementation"></span><h2>Implementation<a class="headerlink" href="#implementation" title="Permalink to this headline"></a></h2>
<p>This design is shown in python API.
Each metric operator need to caculate the metric statistic and return the batch aware states, Python side responsible for accumulate the states for each pass.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Evaluator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Evaluator Base class.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Different evaluator may has different metric states. E.g, Accuracy need two variables, total and right sample counts.</span>
<span class="sd"> Auc need four variables, `true_positives`,</span>
<span class="sd"> `true_negatives`, `false_positives` and `false_negatives`. So every evaluator should create its needed variables and append to main_program</span>
<span class="sd"> The initialization of Evaluator should be responsible for:</span>
<span class="sd"> create metric states and append to the main_program</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">pass</span>
<span class="k">def</span> <span class="nf">_update_ops</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Add mini-batch evaluator caculate operators to the main_program.</span>
<span class="sd"> Add increment operator to accumulate the metric states.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">executor</span><span class="p">,</span> <span class="n">reset_program</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reset metric states at the begin of each pass/user specified batch number.</span>
<span class="sd"> Execute the reset_program to reset the states.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">executor</span><span class="p">,</span> <span class="n">eval_program</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.</span>
<span class="sd"> Execute the eval_program and return the result.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">eval_result</span>
</pre></div>
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develop/doc_cn/_sources/design/evaluator.md.txt 0 → 100644
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## Evaluator Design
### The Problem
During training or serving, we provide the evaluation function to measure the model performance, e.g., accuracy, precision. In the operator based framework design, the data go through the network pipeline batch by batch. As a result, inside the operator, we only can calculate one minibatch metrics. We need to provide a mechanism to calculate the metrics for each N pass/batch the user wanted.
### Evaluator Design
Currently, every operation is expressed in the graph. we divide the evaluator process into three steps.
1. Initialize the metric state and add it into the block.
2. Calculate the statistic of the metric state in every mini-batch. The single operator is only responsible for calculating necessary statistics for one mini-batch. For example, accuracy operator only calculate a minibatch data if run once.
3. Merge the mini-batch statistics to form the evaluation result for multiple mini-batches. When it comes to distributed training/Multi-GPU training, aggregate the value from different devices.
### Implementation
This design is shown in python API.
Each metric operator need to caculate the metric statistic and return the batch aware states, Python side responsible for accumulate the states for each pass.
```python
class Evaluator(object):
"""
Evaluator Base class.
"""
def __init__(self, name, **kwargs):
"""
Different evaluator may has different metric states. E.g, Accuracy need two variables, total and right sample counts.
Auc need four variables, `true_positives`,
`true_negatives`, `false_positives` and `false_negatives`. So every evaluator should create its needed variables and append to main_program
The initialization of Evaluator should be responsible for:
create metric states and append to the main_program
"""
pass
def _update_ops(self, input, label, **kwargs)
"""
Add mini-batch evaluator caculate operators to the main_program.
Add increment operator to accumulate the metric states.
"""
def reset(self, executor, reset_program=None):
"""
Reset metric states at the begin of each pass/user specified batch number.
Execute the reset_program to reset the states.
"""
def eval(self, executor, eval_program=None):
"""
Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
Execute the eval_program and return the result.
"""
return eval_result
```
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<div class="section" id="evaluator-design">
<span id="evaluator-design"></span><h1>Evaluator Design<a class="headerlink" href="#evaluator-design" title="永久链接至标题"></a></h1>
<div class="section" id="the-problem">
<span id="the-problem"></span><h2>The Problem<a class="headerlink" href="#the-problem" title="永久链接至标题"></a></h2>
<p>During training or serving, we provide the evaluation function to measure the model performance, e.g., accuracy, precision. In the operator based framework design, the data go through the network pipeline batch by batch. As a result, inside the operator, we only can calculate one minibatch metrics. We need to provide a mechanism to calculate the metrics for each N pass/batch the user wanted.</p>
</div>
<div class="section" id="evaluator-design">
<span id="id1"></span><h2>Evaluator Design<a class="headerlink" href="#evaluator-design" title="永久链接至标题"></a></h2>
<p>Currently, every operation is expressed in the graph. we divide the evaluator process into three steps.</p>
<ol class="simple">
<li>Initialize the metric state and add it into the block.</li>
<li>Calculate the statistic of the metric state in every mini-batch. The single operator is only responsible for calculating necessary statistics for one mini-batch. For example, accuracy operator only calculate a minibatch data if run once.</li>
</ol>
<ol class="simple">
<li>Merge the mini-batch statistics to form the evaluation result for multiple mini-batches. When it comes to distributed training/Multi-GPU training, aggregate the value from different devices.</li>
</ol>
</div>
<div class="section" id="implementation">
<span id="implementation"></span><h2>Implementation<a class="headerlink" href="#implementation" title="永久链接至标题"></a></h2>
<p>This design is shown in python API.
Each metric operator need to caculate the metric statistic and return the batch aware states, Python side responsible for accumulate the states for each pass.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Evaluator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Evaluator Base class.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Different evaluator may has different metric states. E.g, Accuracy need two variables, total and right sample counts.</span>
<span class="sd"> Auc need four variables, `true_positives`,</span>
<span class="sd"> `true_negatives`, `false_positives` and `false_negatives`. So every evaluator should create its needed variables and append to main_program</span>
<span class="sd"> The initialization of Evaluator should be responsible for:</span>
<span class="sd"> create metric states and append to the main_program</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">pass</span>
<span class="k">def</span> <span class="nf">_update_ops</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Add mini-batch evaluator caculate operators to the main_program.</span>
<span class="sd"> Add increment operator to accumulate the metric states.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">executor</span><span class="p">,</span> <span class="n">reset_program</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reset metric states at the begin of each pass/user specified batch number.</span>
<span class="sd"> Execute the reset_program to reset the states.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">executor</span><span class="p">,</span> <span class="n">eval_program</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.</span>
<span class="sd"> Execute the eval_program and return the result.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">eval_result</span>
</pre></div>
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