#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.utils.ssd_utils</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">sqrt</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.utils.detection_utils</span> <span class="kn">import</span> <span class="n">non_max_suppression</span><span class="p">,</span> <span class="n">NMS_Type</span><span class="p">,</span> \
    <span class="n">matrix_non_max_suppression</span><span class="p">,</span> <span class="n">DetectionPostPredictionCallback</span>
<div class="viewcode-block" id="DefaultBoxes"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.ssd_utils.DefaultBoxes">[docs]</a><span class="k">class</span> <span class="nc">DefaultBoxes</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Default Boxes, (aka: anchor boxes or priors boxes) used by SSD model</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">fig_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">feat_size</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">scales</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">aspect_ratios</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]],</span>
                 <span class="n">scale_xy</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">scale_wh</span><span class="o">=</span><span class="mf">0.2</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        For each feature map i (each predicting level, grids) the anchors (a.k.a. default boxes) will be:</span>
<span class="sd">        [</span>
<span class="sd">            [s, s], [sqrt(s * s_next), sqrt(s * s_next)],</span>
<span class="sd">            [s * sqrt(alpha1), s / sqrt(alpha1)], [s / sqrt(alpha1), s * sqrt(alpha1)],</span>
<span class="sd">            ...</span>
<span class="sd">            [s * sqrt(alphaN), s / sqrt(alphaN)], [s / sqrt(alphaN), s * sqrt(alphaN)]</span>
<span class="sd">        ] / fig_size</span>
<span class="sd">        where:</span>
<span class="sd">            * s = scale[i] - this level&#39;s scale</span>
<span class="sd">            * s_next = scale[i + 1] - next level&#39;s scale</span>
<span class="sd">            * alpha1, ... alphaN - this level&#39;s alphas, e.g. [2, 3]</span>
<span class="sd">            * fig_size - input image resolution</span>
<span class="sd">        Because of division by image resolution, the anchors will be in image coordinates normalized to [0, 1]</span>
<span class="sd">        :param fig_size:        input image resolution</span>
<span class="sd">        :param feat_size:       resolution of all feature maps with predictions (grids)</span>
<span class="sd">        :param scales:          anchor sizes in pixels for each feature level;</span>
<span class="sd">                                one value per level will be used to generate anchors based on the formula above</span>
<span class="sd">        :param aspect_ratios:   lists of alpha values for each feature map</span>
<span class="sd">        :param scale_xy:        predicted boxes will be with a factor scale_xy</span>
<span class="sd">                                so will be multiplied by scale_xy during post-prediction processing;</span>
<span class="sd">                                e.g. scale 0.1 means that prediction will be 10 times bigger</span>
<span class="sd">                                (improves predictions quality)</span>
<span class="sd">        :param scale_wh:        same logic as in scale_xy, but for width and height.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">feat_size</span> <span class="o">=</span> <span class="n">feat_size</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fig_size</span> <span class="o">=</span> <span class="n">fig_size</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">scale_xy_</span> <span class="o">=</span> <span class="n">scale_xy</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">scale_wh_</span> <span class="o">=</span> <span class="n">scale_wh</span>
        <span class="c1"># According to https://github.com/weiliu89/caffe</span>
        <span class="c1"># Calculation method slightly different from paper</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">scales</span> <span class="o">=</span> <span class="n">scales</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">aspect_ratios</span> <span class="o">=</span> <span class="n">aspect_ratios</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">default_boxes</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_anchors</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="c1"># size of feature and number of feature</span>
        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">sfeat</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">feat_size</span><span class="p">):</span>
            <span class="n">sk1</span> <span class="o">=</span> <span class="n">scales</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
            <span class="n">sk2</span> <span class="o">=</span> <span class="n">scales</span><span class="p">[</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
            <span class="n">sk3</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">sk1</span> <span class="o">*</span> <span class="n">sk2</span><span class="p">)</span>
            <span class="n">all_sizes</span> <span class="o">=</span> <span class="p">[(</span><span class="n">sk1</span><span class="p">,</span> <span class="n">sk1</span><span class="p">),</span> <span class="p">(</span><span class="n">sk3</span><span class="p">,</span> <span class="n">sk3</span><span class="p">)]</span>
            <span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">aspect_ratios</span><span class="p">[</span><span class="n">idx</span><span class="p">]:</span>
                <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="n">sk1</span> <span class="o">*</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">alpha</span><span class="p">),</span> <span class="n">sk1</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span>
                <span class="n">all_sizes</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
                <span class="n">all_sizes</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
            <span class="n">all_sizes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">all_sizes</span><span class="p">)</span> <span class="o">/</span> <span class="n">fig_size</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">num_anchors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">all_sizes</span><span class="p">))</span>
            <span class="k">for</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="ow">in</span> <span class="n">all_sizes</span><span class="p">:</span>
                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">sfeat</span><span class="p">),</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
                    <span class="n">cx</span><span class="p">,</span> <span class="n">cy</span> <span class="o">=</span> <span class="p">(</span><span class="n">j</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">/</span> <span class="n">sfeat</span><span class="p">,</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">/</span> <span class="n">sfeat</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">default_boxes</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">cx</span><span class="p">,</span> <span class="n">cy</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">default_boxes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="o">.</span><span class="n">clamp_</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="c1"># For IoU calculation</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xyxy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xyxy</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xyxy</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xyxy</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xyxy</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span>
    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">scale_xy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_xy_</span>
    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">scale_wh</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_wh_</span>
    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="s2">&quot;xyxy&quot;</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">order</span> <span class="o">==</span> <span class="s2">&quot;xyxy&quot;</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes_xyxy</span>
        <span class="k">if</span> <span class="n">order</span> <span class="o">==</span> <span class="s2">&quot;xywh&quot;</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dboxes</span></div>
<div class="viewcode-block" id="SSDPostPredictCallback"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.ssd_utils.SSDPostPredictCallback">[docs]</a><span class="k">class</span> <span class="nc">SSDPostPredictCallback</span><span class="p">(</span><span class="n">DetectionPostPredictionCallback</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    post prediction callback module to convert and filter predictions coming from the SSD net to a format</span>
<span class="sd">    used by all other detection models</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">conf</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.001</span><span class="p">,</span> <span class="n">iou</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.6</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
                 <span class="n">max_predictions</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">300</span><span class="p">,</span>
                 <span class="n">nms_type</span><span class="p">:</span> <span class="n">NMS_Type</span> <span class="o">=</span> <span class="n">NMS_Type</span><span class="o">.</span><span class="n">ITERATIVE</span><span class="p">,</span>
                 <span class="n">multi_label_per_box</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Predictions of SSD contain unnormalized probabilities for a background class,</span>
<span class="sd">        together with confidences for all the dataset classes. Background will be utilized and discarded,</span>
<span class="sd">        so this callback will return 0-based classes without background</span>
<span class="sd">        :param conf: confidence threshold</span>
<span class="sd">        :param iou: IoU threshold</span>
<span class="sd">        :param classes: (optional list) filter by class</span>
<span class="sd">        :param nms_type: the type of nms to use (iterative or matrix)</span>
<span class="sd">        :param multi_label_per_box: whether to use re-use each box with all possible labels</span>
<span class="sd">                                    (instead of the maximum confidence all confidences above threshold</span>
<span class="sd">                                    will be sent to NMS)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">SSDPostPredictCallback</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">conf</span> <span class="o">=</span> <span class="n">conf</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">iou</span> <span class="o">=</span> <span class="n">iou</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">nms_type</span> <span class="o">=</span> <span class="n">nms_type</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">classes</span> <span class="o">=</span> <span class="n">classes</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">max_predictions</span> <span class="o">=</span> <span class="n">max_predictions</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">multi_label_per_box</span> <span class="o">=</span> <span class="n">multi_label_per_box</span>
<div class="viewcode-block" id="SSDPostPredictCallback.forward"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.ssd_utils.SSDPostPredictCallback.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="n">nms_input</span> <span class="o">=</span> <span class="n">predictions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nms_type</span> <span class="o">==</span> <span class="n">NMS_Type</span><span class="o">.</span><span class="n">ITERATIVE</span><span class="p">:</span>
            <span class="n">nms_res</span> <span class="o">=</span> <span class="n">non_max_suppression</span><span class="p">(</span><span class="n">nms_input</span><span class="p">,</span> <span class="n">conf_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">conf</span><span class="p">,</span> <span class="n">iou_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">iou</span><span class="p">,</span>
                                          <span class="n">multi_label_per_box</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">multi_label_per_box</span><span class="p">,</span> <span class="n">with_confidence</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">nms_res</span> <span class="o">=</span> <span class="n">matrix_non_max_suppression</span><span class="p">(</span><span class="n">nms_input</span><span class="p">,</span> <span class="n">conf_thres</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">conf</span><span class="p">,</span>
                                                 <span class="n">max_num_of_detections</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">max_predictions</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_filter_max_predictions</span><span class="p">(</span><span class="n">nms_res</span><span class="p">)</span></div>
    <span class="k">def</span> <span class="nf">_filter_max_predictions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">res</span><span class="p">:</span> <span class="n">List</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">:</span>
        <span class="n">res</span><span class="p">[:]</span> <span class="o">=</span> <span class="p">[</span><span class="n">im</span><span class="p">[:</span><span class="bp">self</span><span class="o">.</span><span class="n">max_predictions</span><span class="p">]</span> <span class="k">if</span> <span class="p">(</span><span class="n">im</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">im</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_predictions</span><span class="p">)</span> <span class="k">else</span> <span class="n">im</span> <span class="k">for</span> <span class="n">im</span> <span class="ow">in</span> <span class="n">res</span><span class="p">]</span>
        <span class="k">return</span> <span class="n">res</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
