#396 Trainer constructor cleanup

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Ghost merged 1 commits into Deci-AI:master from deci-ai:feature/SG-266_clean_trainer_ctor
  
    
        
          
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  <h1>Source code for super_gradients.training.metrics.detection_metrics</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torchmetrics</span> <span class="kn">import</span> <span class="n">Metric</span>
<span class="kn">import</span> <span class="nn">super_gradients</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils</span> <span class="kn">import</span> <span class="n">tensor_container_to_device</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils.detection_utils</span> <span class="kn">import</span> <span class="n">compute_detection_matching</span><span class="p">,</span> <span class="n">compute_detection_metrics</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils.detection_utils</span> <span class="kn">import</span> <span class="n">DetectionPostPredictionCallback</span><span class="p">,</span> <span class="n">IouThreshold</span>
<span class="kn">from</span> <span class="nn">super_gradients.common.abstractions.abstract_logger</span> <span class="kn">import</span> <span class="n">get_logger</span>
<span class="n">logger</span> <span class="o">=</span> <span class="n">get_logger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
<div class="viewcode-block" id="DetectionMetrics"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.DetectionMetrics">[docs]</a><span class="k">class</span> <span class="nc">DetectionMetrics</span><span class="p">(</span><span class="n">Metric</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    DetectionMetrics</span>
<span class="sd">    Metric class for computing F1, Precision, Recall and Mean Average Precision.</span>
<span class="sd">    Attributes:</span>
<span class="sd">         num_cls:                  Number of classes.</span>
<span class="sd">         post_prediction_callback: DetectionPostPredictionCallback to be applied on net&#39;s output prior</span>
<span class="sd">                                   to the metric computation (NMS).</span>
<span class="sd">         normalize_targets:        Whether to normalize bbox coordinates by image size (default=False).</span>
<span class="sd">         iou_thresholds:    IoU threshold to compute the mAP (default=torch.linspace(0.5, 0.95, 10)).</span>
<span class="sd">         recall_thresholds: Recall threshold to compute the mAP (default=torch.linspace(0, 1, 101)).</span>
<span class="sd">         score_threshold:   Score threshold to compute Recall, Precision and F1 (default=0.1)</span>
<span class="sd">         top_k_predictions: Number of predictions per class used to compute metrics, ordered by confidence score</span>
<span class="sd">                            (default=100)</span>
<span class="sd">         dist_sync_on_step: Synchronize metric state across processes at each ``forward()``</span>
<span class="sd">                            before returning the value at the step. (default=False)</span>
<span class="sd">        accumulate_on_cpu:     Run on CPU regardless of device used in other parts.</span>
<span class="sd">                            This is to avoid &quot;CUDA out of memory&quot; that might happen on GPU (default False)</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">num_cls</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                 <span class="n">post_prediction_callback</span><span class="p">:</span> <span class="n">DetectionPostPredictionCallback</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
                 <span class="n">normalize_targets</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
                 <span class="n">iou_thres</span><span class="p">:</span> <span class="n">IouThreshold</span> <span class="o">=</span> <span class="n">IouThreshold</span><span class="o">.</span><span class="n">MAP_05_TO_095</span><span class="p">,</span>
                 <span class="n">recall_thres</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
                 <span class="n">score_thres</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span>
                 <span class="n">top_k_predictions</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span>
                 <span class="n">dist_sync_on_step</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
                 <span class="n">accumulate_on_cpu</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">dist_sync_on_step</span><span class="o">=</span><span class="n">dist_sync_on_step</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_cls</span> <span class="o">=</span> <span class="n">num_cls</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">iou_thres</span> <span class="o">=</span> <span class="n">iou_thres</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">map_str</span> <span class="o">=</span> <span class="s1">&#39;mAP@</span><span class="si">%.1f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">iou_thres</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">iou_thres</span><span class="o">.</span><span class="n">is_range</span><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;mAP@</span><span class="si">%.2f</span><span class="s1">:</span><span class="si">%.2f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">iou_thres</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">component_names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Precision&quot;</span><span class="p">,</span> <span class="s2">&quot;Recall&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">map_str</span><span class="p">,</span> <span class="s2">&quot;F1&quot;</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">components</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">component_names</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">post_prediction_callback</span> <span class="o">=</span> <span class="n">post_prediction_callback</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">is_distributed</span> <span class="o">=</span> <span class="n">super_gradients</span><span class="o">.</span><span class="n">is_distributed</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">denormalize_targets</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">normalize_targets</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">rank</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_state</span><span class="p">(</span><span class="s2">&quot;matching_info&quot;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="p">[],</span> <span class="n">dist_reduce_fx</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">iou_thresholds</span> <span class="o">=</span> <span class="n">iou_thres</span><span class="o">.</span><span class="n">to_tensor</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">recall_thresholds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</span> <span class="k">if</span> <span class="n">recall_thres</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">recall_thres</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">score_threshold</span> <span class="o">=</span> <span class="n">score_thres</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">top_k_predictions</span> <span class="o">=</span> <span class="n">top_k_predictions</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">accumulate_on_cpu</span> <span class="o">=</span> <span class="n">accumulate_on_cpu</span>
<div class="viewcode-block" id="DetectionMetrics.update"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.DetectionMetrics.update">[docs]</a>    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">target</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
               <span class="n">inputs</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">,</span> <span class="n">crowd_targets</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Apply NMS and match all the predictions and targets of a given batch, and update the metric state accordingly.</span>
<span class="sd">        :param preds :        Raw output of the model, the format might change from one model to another, but has to fit</span>
<span class="sd">                                the input format of the post_prediction_callback</span>
<span class="sd">        :param target:        Targets for all images of shape (total_num_targets, 6)</span>
<span class="sd">                                format:  (index, x, y, w, h, label) where x,y,w,h are in range [0,1]</span>
<span class="sd">        :param device:        Device to run on</span>
<span class="sd">        :param inputs:        Input image tensor of shape (batch_size, n_img, height, width)</span>
<span class="sd">        :param crowd_targets: Crowd targets for all images of shape (total_num_targets, 6)</span>
<span class="sd">                                 format:  (index, x, y, w, h, label) where x,y,w,h are in range [0,1]</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">iou_thresholds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">iou_thresholds</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
        <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">shape</span>
        <span class="n">targets</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
        <span class="n">crowd_targets</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> <span class="k">if</span> <span class="n">crowd_targets</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">crowd_targets</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
        <span class="n">preds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">post_prediction_callback</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
        <span class="n">new_matching_info</span> <span class="o">=</span> <span class="n">compute_detection_matching</span><span class="p">(</span>
            <span class="n">preds</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">iou_thresholds</span><span class="p">,</span> <span class="n">crowd_targets</span><span class="o">=</span><span class="n">crowd_targets</span><span class="p">,</span>
            <span class="n">top_k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">top_k_predictions</span><span class="p">,</span> <span class="n">denormalize_targets</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">denormalize_targets</span><span class="p">,</span>
            <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">return_on_cpu</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">accumulate_on_cpu</span><span class="p">)</span>
        <span class="n">accumulated_matching_info</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;matching_info&quot;</span><span class="p">)</span>
        <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;matching_info&quot;</span><span class="p">,</span> <span class="n">accumulated_matching_info</span> <span class="o">+</span> <span class="n">new_matching_info</span><span class="p">)</span></div>
<div class="viewcode-block" id="DetectionMetrics.compute"><a class="viewcode-back" href="../../../../super_gradients.training.metrics.html#super_gradients.training.metrics.DetectionMetrics.compute">[docs]</a>    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]:</span>
        <span class="sd">&quot;&quot;&quot;Compute the metrics for all the accumulated results.</span>
<span class="sd">            :return: Metrics of interest</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">mean_ap</span><span class="p">,</span> <span class="n">mean_precision</span><span class="p">,</span> <span class="n">mean_recall</span><span class="p">,</span> <span class="n">mean_f1</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span>
        <span class="n">accumulated_matching_info</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;matching_info&quot;</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">accumulated_matching_info</span><span class="p">):</span>
            <span class="n">matching_info_tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">accumulated_matching_info</span><span class="p">))]</span>
            <span class="c1"># shape (n_class, nb_iou_thresh)</span>
            <span class="n">ap</span><span class="p">,</span> <span class="n">precision</span><span class="p">,</span> <span class="n">recall</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">unique_classes</span> <span class="o">=</span> <span class="n">compute_detection_metrics</span><span class="p">(</span>
                <span class="o">*</span><span class="n">matching_info_tensors</span><span class="p">,</span> <span class="n">recall_thresholds</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">recall_thresholds</span><span class="p">,</span> <span class="n">score_threshold</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">score_threshold</span><span class="p">,</span>
                <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">accumulate_on_cpu</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
            <span class="c1"># Precision, recall and f1 are computed for smallest IoU threshold (usually 0.5), averaged over classes</span>
            <span class="n">mean_precision</span><span class="p">,</span> <span class="n">mean_recall</span><span class="p">,</span> <span class="n">mean_f1</span> <span class="o">=</span> <span class="n">precision</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">recall</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">f1</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
            <span class="c1"># MaP is averaged over IoU thresholds and over classes</span>
            <span class="n">mean_ap</span> <span class="o">=</span> <span class="n">ap</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;Precision&quot;</span><span class="p">:</span> <span class="n">mean_precision</span><span class="p">,</span> <span class="s2">&quot;Recall&quot;</span><span class="p">:</span> <span class="n">mean_recall</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">map_str</span><span class="p">:</span> <span class="n">mean_ap</span><span class="p">,</span> <span class="s2">&quot;F1&quot;</span><span class="p">:</span> <span class="n">mean_f1</span><span class="p">}</span></div>
    <span class="k">def</span> <span class="nf">_sync_dist</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dist_sync_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">process_group</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        When in distributed mode, stats are aggregated after each forward pass to the metric state. Since these have all</span>
<span class="sd">        different sizes we override the synchronization function since it works only for tensors (and use</span>
<span class="sd">        all_gather_object)</span>
<span class="sd">        @param dist_sync_fn:</span>
<span class="sd">        @return:</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">get_world_size</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_distributed</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">rank</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">rank</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">get_rank</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_distributed</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_distributed</span><span class="p">:</span>
            <span class="n">local_state_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">attr</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attr</span><span class="p">)</span> <span class="k">for</span> <span class="n">attr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reductions</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span>
            <span class="n">gathered_state_dicts</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">all_gather_object</span><span class="p">(</span><span class="n">gathered_state_dicts</span><span class="p">,</span> <span class="n">local_state_dict</span><span class="p">)</span>
            <span class="n">matching_info</span> <span class="o">=</span> <span class="p">[]</span>
            <span class="k">for</span> <span class="n">state_dict</span> <span class="ow">in</span> <span class="n">gathered_state_dicts</span><span class="p">:</span>
                <span class="n">matching_info</span> <span class="o">+=</span> <span class="n">state_dict</span><span class="p">[</span><span class="s2">&quot;matching_info&quot;</span><span class="p">]</span>
            <span class="n">matching_info</span> <span class="o">=</span> <span class="n">tensor_container_to_device</span><span class="p">(</span><span class="n">matching_info</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">accumulate_on_cpu</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
            <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;matching_info&quot;</span><span class="p">,</span> <span class="n">matching_info</span><span class="p">)</span></div>
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Deci-AI / super-gradients connected to https://github.com/Deci-AI/super-gradients.git

#396 Trainer constructor cleanup

Deci-AI
/
super-gradients
connected to https://github.com/Deci-AI/super-gradients.git
