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


  
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  <h1>Source code for super_gradients.training.utils.checkpoint_utils</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">tempfile</span>
<span class="kn">import</span> <span class="nn">pkg_resources</span>

<span class="kn">import</span> <span class="nn">torch</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="kn">from</span> <span class="nn">super_gradients.common</span> <span class="kn">import</span> <span class="n">explicit_params_validation</span><span class="p">,</span> <span class="n">ADNNModelRepositoryDataInterfaces</span>
<span class="kn">from</span> <span class="nn">super_gradients.training.pretrained_models</span> <span class="kn">import</span> <span class="n">MODEL_URLS</span>
<span class="kn">from</span> <span class="nn">super_gradients.common.environment</span> <span class="kn">import</span> <span class="n">environment_config</span>

<span class="k">try</span><span class="p">:</span>
    <span class="kn">from</span> <span class="nn">torch.hub</span> <span class="kn">import</span> <span class="n">download_url_to_file</span><span class="p">,</span> <span class="n">load_state_dict_from_url</span>
<span class="k">except</span> <span class="p">(</span><span class="ne">ModuleNotFoundError</span><span class="p">,</span> <span class="ne">ImportError</span><span class="p">,</span> <span class="ne">NameError</span><span class="p">):</span>
    <span class="kn">from</span> <span class="nn">torch.hub</span> <span class="kn">import</span> <span class="n">_download_url_to_file</span> <span class="k">as</span> <span class="n">download_url_to_file</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>


<span class="k">def</span> <span class="nf">get_checkpoints_dir_path</span><span class="p">(</span><span class="n">experiment_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">ckpt_root_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Creating the checkpoint directory of a given experiment.</span>
<span class="sd">    :param experiment_name:     Name of the experiment.</span>
<span class="sd">    :param ckpt_root_dir:       Local root directory path where all experiment logging directories will</span>
<span class="sd">                                reside. When none is give, it is assumed that pkg_resources.resource_filename(&#39;checkpoints&#39;, &quot;&quot;)</span>
<span class="sd">                                exists and will be used.</span>
<span class="sd">    :return:                    checkpoints_dir_path</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">ckpt_root_dir</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ckpt_root_dir</span><span class="p">,</span> <span class="n">experiment_name</span><span class="p">)</span>
    <span class="k">elif</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">environment_config</span><span class="o">.</span><span class="n">PKG_CHECKPOINTS_DIR</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">environment_config</span><span class="o">.</span><span class="n">PKG_CHECKPOINTS_DIR</span><span class="p">,</span> <span class="n">experiment_name</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Illegal checkpoints directory: pass ckpt_root_dir that exists, or add &#39;checkpoints&#39; to resources.&quot;</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">get_ckpt_local_path</span><span class="p">(</span><span class="n">source_ckpt_folder_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">experiment_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">ckpt_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">external_checkpoint_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Gets the local path to the checkpoint file, which will be:</span>
<span class="sd">        - By default: YOUR_REPO_ROOT/super_gradients/checkpoints/experiment_name.</span>
<span class="sd">        - if the checkpoint file is remotely located:</span>
<span class="sd">            when overwrite_local_checkpoint=True then it will be saved in a temporary path which will be returned,</span>
<span class="sd">            otherwise it will be downloaded to YOUR_REPO_ROOT/super_gradients/checkpoints/experiment_name and overwrite</span>
<span class="sd">            YOUR_REPO_ROOT/super_gradients/checkpoints/experiment_name/ckpt_name if such file exists.</span>
<span class="sd">        - external_checkpoint_path when external_checkpoint_path != None</span>

<span class="sd">    @param source_ckpt_folder_name: The folder where the checkpoint is saved. When set to None- uses the experiment_name.</span>
<span class="sd">    @param experiment_name: experiment name attr in trainer</span>
<span class="sd">    @param ckpt_name: checkpoint filename</span>
<span class="sd">    @param external_checkpoint_path: full path to checkpoint file (that might be located outside of super_gradients/checkpoints directory)</span>
<span class="sd">    @return:</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">external_checkpoint_path</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">external_checkpoint_path</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">checkpoints_folder_name</span> <span class="o">=</span> <span class="n">source_ckpt_folder_name</span> <span class="ow">or</span> <span class="n">experiment_name</span>
        <span class="n">checkpoints_dir_path</span> <span class="o">=</span> <span class="n">get_checkpoints_dir_path</span><span class="p">(</span><span class="n">checkpoints_folder_name</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoints_dir_path</span><span class="p">,</span> <span class="n">ckpt_name</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">adaptive_load_state_dict</span><span class="p">(</span><span class="n">net</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">strict</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Adaptively loads state_dict to net, by adapting the state_dict to net&#39;s layer names first.</span>

<span class="sd">    @param net: (nn.Module) to load state_dict to</span>
<span class="sd">    @param state_dict: (dict) Chekpoint state_dict</span>
<span class="sd">    @param strict: (str) key matching strictness</span>
<span class="sd">    @return:</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">try</span><span class="p">:</span>
        <span class="n">net</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">[</span><span class="s2">&quot;net&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;net&quot;</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">else</span> <span class="n">state_dict</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="n">strict</span><span class="p">)</span>
    <span class="k">except</span> <span class="p">(</span><span class="ne">RuntimeError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="ne">KeyError</span><span class="p">)</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">strict</span> <span class="o">==</span> <span class="s2">&quot;no_key_matching&quot;</span><span class="p">:</span>
            <span class="n">adapted_state_dict</span> <span class="o">=</span> <span class="n">adapt_state_dict_to_fit_model_layer_names</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">state_dict</span><span class="p">)</span>
            <span class="n">net</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">adapted_state_dict</span><span class="p">[</span><span class="s2">&quot;net&quot;</span><span class="p">],</span> <span class="n">strict</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">raise_informative_runtime_error</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">state_dict</span><span class="p">,</span> <span class="n">ex</span><span class="p">)</span>


<span class="nd">@explicit_params_validation</span><span class="p">(</span><span class="n">validation_type</span><span class="o">=</span><span class="s2">&quot;None&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">copy_ckpt_to_local_folder</span><span class="p">(</span>
    <span class="n">local_ckpt_destination_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
    <span class="n">ckpt_filename</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
    <span class="n">remote_ckpt_source_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">path_src</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;local&quot;</span><span class="p">,</span>
    <span class="n">overwrite_local_ckpt</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">load_weights_only</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="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Copy the checkpoint from any supported source to a local destination path</span>
<span class="sd">        :param local_ckpt_destination_dir:  destination where the checkpoint will be saved to</span>
<span class="sd">        :param ckpt_filename:         ckpt_best.pth Or ckpt_latest.pth</span>
<span class="sd">        :param remote_ckpt_source_dir:       Name of the source checkpoint to be loaded (S3 Model\full URL)</span>
<span class="sd">        :param path_src:              S3 / url</span>
<span class="sd">        :param overwrite_local_ckpt:  determines if checkpoint will be saved in destination dir or in a temp folder</span>

<span class="sd">        :return: Path to checkpoint</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">ckpt_file_full_local_path</span> <span class="o">=</span> <span class="kc">None</span>

    <span class="c1"># IF NOT DEFINED - IT IS SET TO THE TARGET&#39;s FOLDER NAME</span>
    <span class="n">remote_ckpt_source_dir</span> <span class="o">=</span> <span class="n">local_ckpt_destination_dir</span> <span class="k">if</span> <span class="n">remote_ckpt_source_dir</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">remote_ckpt_source_dir</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="n">overwrite_local_ckpt</span><span class="p">:</span>
        <span class="c1"># CREATE A TEMP FOLDER TO SAVE THE CHECKPOINT TO</span>
        <span class="n">download_ckpt_destination_dir</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">gettempdir</span><span class="p">()</span>
        <span class="nb">print</span><span class="p">(</span>
            <span class="s2">&quot;PLEASE NOTICE - YOU ARE IMPORTING A REMOTE CHECKPOINT WITH overwrite_local_checkpoint = False &quot;</span>
            <span class="s2">&quot;-&gt; IT WILL BE REDIRECTED TO A TEMP FOLDER AND DELETED ON MACHINE RESTART&quot;</span>
        <span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="c1"># SAVE THE CHECKPOINT TO MODEL&#39;s FOLDER</span>
        <span class="n">download_ckpt_destination_dir</span> <span class="o">=</span> <span class="n">pkg_resources</span><span class="o">.</span><span class="n">resource_filename</span><span class="p">(</span><span class="s2">&quot;checkpoints&quot;</span><span class="p">,</span> <span class="n">local_ckpt_destination_dir</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">path_src</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;s3&quot;</span><span class="p">):</span>
        <span class="n">model_checkpoints_data_interface</span> <span class="o">=</span> <span class="n">ADNNModelRepositoryDataInterfaces</span><span class="p">(</span><span class="n">data_connection_location</span><span class="o">=</span><span class="n">path_src</span><span class="p">)</span>
        <span class="c1"># DOWNLOAD THE FILE FROM S3 TO THE DESTINATION FOLDER</span>
        <span class="n">ckpt_file_full_local_path</span> <span class="o">=</span> <span class="n">model_checkpoints_data_interface</span><span class="o">.</span><span class="n">load_remote_checkpoints_file</span><span class="p">(</span>
            <span class="n">ckpt_source_remote_dir</span><span class="o">=</span><span class="n">remote_ckpt_source_dir</span><span class="p">,</span>
            <span class="n">ckpt_destination_local_dir</span><span class="o">=</span><span class="n">download_ckpt_destination_dir</span><span class="p">,</span>
            <span class="n">ckpt_file_name</span><span class="o">=</span><span class="n">ckpt_filename</span><span class="p">,</span>
            <span class="n">overwrite_local_checkpoints_file</span><span class="o">=</span><span class="n">overwrite_local_ckpt</span><span class="p">,</span>
        <span class="p">)</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="n">load_weights_only</span><span class="p">:</span>
            <span class="c1"># COPY LOG FILES FROM THE REMOTE DIRECTORY TO THE LOCAL ONE ONLY IF LOADING THE CURRENT MODELs CKPT</span>
            <span class="n">model_checkpoints_data_interface</span><span class="o">.</span><span class="n">load_all_remote_log_files</span><span class="p">(</span>
                <span class="n">model_name</span><span class="o">=</span><span class="n">remote_ckpt_source_dir</span><span class="p">,</span> <span class="n">model_checkpoint_local_dir</span><span class="o">=</span><span class="n">download_ckpt_destination_dir</span>
            <span class="p">)</span>

    <span class="k">if</span> <span class="n">path_src</span> <span class="o">==</span> <span class="s2">&quot;url&quot;</span><span class="p">:</span>
        <span class="n">ckpt_file_full_local_path</span> <span class="o">=</span> <span class="n">download_ckpt_destination_dir</span> <span class="o">+</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">sep</span> <span class="o">+</span> <span class="n">ckpt_filename</span>
        <span class="c1"># DOWNLOAD THE FILE FROM URL TO THE DESTINATION FOLDER</span>
        <span class="n">download_url_to_file</span><span class="p">(</span><span class="n">remote_ckpt_source_dir</span><span class="p">,</span> <span class="n">ckpt_file_full_local_path</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">ckpt_file_full_local_path</span>


<span class="k">def</span> <span class="nf">read_ckpt_state_dict</span><span class="p">(</span><span class="n">ckpt_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">):</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">ckpt_path</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Incorrect Checkpoint path&quot;</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;cuda&quot;</span><span class="p">:</span>
        <span class="n">state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">ckpt_path</span><span class="p">)</span>

    <span class="k">else</span><span class="p">:</span>
        <span class="n">state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">ckpt_path</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="k">lambda</span> <span class="n">storage</span><span class="p">,</span> <span class="n">loc</span><span class="p">:</span> <span class="n">storage</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">state_dict</span>


<div class="viewcode-block" id="adapt_state_dict_to_fit_model_layer_names"><a class="viewcode-back" href="../../../../super_gradients.training.html#super_gradients.training.utils.adapt_state_dict_to_fit_model_layer_names">[docs]</a><span class="k">def</span> <span class="nf">adapt_state_dict_to_fit_model_layer_names</span><span class="p">(</span><span class="n">model_state_dict</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">source_ckpt</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">exclude</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="p">[],</span> <span class="n">solver</span><span class="p">:</span> <span class="n">callable</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Given a model state dict and source checkpoints, the method tries to correct the keys in the model_state_dict to fit</span>
<span class="sd">    the ckpt in order to properly load the weights into the model. If unsuccessful - returns None</span>
<span class="sd">        :param model_state_dict:               the model state_dict</span>
<span class="sd">        :param source_ckpt:                         checkpoint dict</span>
<span class="sd">        :param exclude                  optional list for excluded layers</span>
<span class="sd">        :param solver:                  callable with signature (ckpt_key, ckpt_val, model_key, model_val)</span>
<span class="sd">                                        that returns a desired weight for ckpt_val.</span>
<span class="sd">        :return: renamed checkpoint dict (if possible)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="s2">&quot;net&quot;</span> <span class="ow">in</span> <span class="n">source_ckpt</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">source_ckpt</span> <span class="o">=</span> <span class="n">source_ckpt</span><span class="p">[</span><span class="s2">&quot;net&quot;</span><span class="p">]</span>
    <span class="n">model_state_dict_excluded</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model_state_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="n">x</span> <span class="ow">in</span> <span class="n">k</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">exclude</span><span class="p">)}</span>
    <span class="n">new_ckpt_dict</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">for</span> <span class="p">(</span><span class="n">ckpt_key</span><span class="p">,</span> <span class="n">ckpt_val</span><span class="p">),</span> <span class="p">(</span><span class="n">model_key</span><span class="p">,</span> <span class="n">model_val</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">source_ckpt</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">model_state_dict_excluded</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
        <span class="k">if</span> <span class="n">solver</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">ckpt_val</span> <span class="o">=</span> <span class="n">solver</span><span class="p">(</span><span class="n">ckpt_key</span><span class="p">,</span> <span class="n">ckpt_val</span><span class="p">,</span> <span class="n">model_key</span><span class="p">,</span> <span class="n">model_val</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">ckpt_val</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">model_val</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ckpt layer </span><span class="si">{</span><span class="n">ckpt_key</span><span class="si">}</span><span class="s2"> with shape </span><span class="si">{</span><span class="n">ckpt_val</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> does not match </span><span class="si">{</span><span class="n">model_key</span><span class="si">}</span><span class="s2">&quot;</span> <span class="sa">f</span><span class="s2">&quot; with shape </span><span class="si">{</span><span class="n">model_val</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> in the model&quot;</span><span class="p">)</span>
        <span class="n">new_ckpt_dict</span><span class="p">[</span><span class="n">model_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">ckpt_val</span>
    <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;net&quot;</span><span class="p">:</span> <span class="n">new_ckpt_dict</span><span class="p">}</span></div>


<div class="viewcode-block" id="raise_informative_runtime_error"><a class="viewcode-back" href="../../../../super_gradients.training.html#super_gradients.training.utils.raise_informative_runtime_error">[docs]</a><span class="k">def</span> <span class="nf">raise_informative_runtime_error</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">checkpoint</span><span class="p">,</span> <span class="n">exception_msg</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Given a model state dict and source checkpoints, the method calls &quot;adapt_state_dict_to_fit_model_layer_names&quot;</span>
<span class="sd">    and enhances the exception_msg if loading the checkpoint_dict via the conversion method is possible</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">try</span><span class="p">:</span>
        <span class="n">new_ckpt_dict</span> <span class="o">=</span> <span class="n">adapt_state_dict_to_fit_model_layer_names</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">checkpoint</span><span class="p">)</span>
        <span class="n">temp_file</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">NamedTemporaryFile</span><span class="p">()</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;.pt&quot;</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">new_ckpt_dict</span><span class="p">,</span> <span class="n">temp_file</span><span class="p">)</span>
        <span class="n">exception_msg</span> <span class="o">=</span> <span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="si">{</span><span class="s1">&#39;=&#39;</span> <span class="o">*</span> <span class="mi">200</span><span class="si">}</span><span class="se">\n</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">exception_msg</span><span class="p">)</span><span class="si">}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">convert ckpt via the utils.adapt_state_dict_to_fit_&quot;</span>
            <span class="sa">f</span><span class="s2">&quot;model_layer_names method</span><span class="se">\n</span><span class="s2">a converted checkpoint file was saved in the path </span><span class="si">{</span><span class="n">temp_file</span><span class="si">}</span><span class="se">\n</span><span class="si">{</span><span class="s1">&#39;=&#39;</span> <span class="o">*</span> <span class="mi">200</span><span class="si">}</span><span class="s2">&quot;</span>
        <span class="p">)</span>
    <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>  <span class="c1"># IN CASE adapt_state_dict_to_fit_model_layer_names WAS UNSUCCESSFUL</span>
        <span class="n">exception_msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="si">{</span><span class="s1">&#39;=&#39;</span> <span class="o">*</span> <span class="mi">200</span><span class="si">}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">The checkpoint and model shapes do no fit, e.g.: </span><span class="si">{</span><span class="n">ex</span><span class="si">}</span><span class="se">\n</span><span class="si">{</span><span class="s1">&#39;=&#39;</span> <span class="o">*</span> <span class="mi">200</span><span class="si">}</span><span class="s2">&quot;</span>
    <span class="k">finally</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="n">exception_msg</span><span class="p">)</span></div>


<span class="k">def</span> <span class="nf">load_checkpoint_to_model</span><span class="p">(</span>
    <span class="n">ckpt_local_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">load_backbone</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="n">net</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">strict</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">load_weights_only</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="n">load_ema_as_net</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="sd">&quot;&quot;&quot;</span>
<span class="sd">    Loads the state dict in ckpt_local_path to net and returns the checkpoint&#39;s state dict.</span>

<span class="sd">    @param load_ema_as_net: Will load the EMA inside the checkpoint file to the network when set</span>
<span class="sd">    @param ckpt_local_path: local path to the checkpoint file</span>
<span class="sd">    @param load_backbone: whether to load the checkpoint as a backbone</span>
<span class="sd">    @param net: network to load the checkpoint to</span>
<span class="sd">    @param strict:</span>
<span class="sd">    @param load_weights_only:</span>
<span class="sd">    @return:</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">ckpt_local_path</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">ckpt_local_path</span><span class="p">):</span>
        <span class="n">error_msg</span> <span class="o">=</span> <span class="s2">&quot;Error - loading Model Checkpoint: Path </span><span class="si">{}</span><span class="s2"> does not exist&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ckpt_local_path</span><span class="p">)</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="n">error_msg</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">load_backbone</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="s2">&quot;backbone&quot;</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No backbone attribute in net - Can&#39;t load backbone weights&quot;</span><span class="p">)</span>

    <span class="c1"># LOAD THE LOCAL CHECKPOINT PATH INTO A state_dict OBJECT</span>
    <span class="n">checkpoint</span> <span class="o">=</span> <span class="n">read_ckpt_state_dict</span><span class="p">(</span><span class="n">ckpt_path</span><span class="o">=</span><span class="n">ckpt_local_path</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">load_ema_as_net</span><span class="p">:</span>
        <span class="k">if</span> <span class="s2">&quot;ema_net&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">checkpoint</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Can&#39;t load ema network- no EMA network stored in checkpoint file&quot;</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">checkpoint</span><span class="p">[</span><span class="s2">&quot;net&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s2">&quot;ema_net&quot;</span><span class="p">]</span>

    <span class="c1"># LOAD THE CHECKPOINTS WEIGHTS TO THE MODEL</span>
    <span class="k">if</span> <span class="n">load_backbone</span><span class="p">:</span>
        <span class="n">adaptive_load_state_dict</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">backbone</span><span class="p">,</span> <span class="n">checkpoint</span><span class="p">,</span> <span class="n">strict</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">adaptive_load_state_dict</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">checkpoint</span><span class="p">,</span> <span class="n">strict</span><span class="p">)</span>

    <span class="n">message_suffix</span> <span class="o">=</span> <span class="s2">&quot; checkpoint.&quot;</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">load_ema_as_net</span> <span class="k">else</span> <span class="s2">&quot; EMA checkpoint.&quot;</span>
    <span class="n">message_model</span> <span class="o">=</span> <span class="s2">&quot;model&quot;</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">load_backbone</span> <span class="k">else</span> <span class="s2">&quot;model&#39;s backbone&quot;</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Successfully loaded &quot;</span> <span class="o">+</span> <span class="n">message_model</span> <span class="o">+</span> <span class="s2">&quot; weights from &quot;</span> <span class="o">+</span> <span class="n">ckpt_local_path</span> <span class="o">+</span> <span class="n">message_suffix</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">load_weights_only</span> <span class="ow">or</span> <span class="n">load_backbone</span><span class="p">:</span>
        <span class="c1"># DISCARD ALL THE DATA STORED IN CHECKPOINT OTHER THAN THE WEIGHTS</span>
        <span class="p">[</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="k">if</span> <span class="n">key</span> <span class="o">!=</span> <span class="s2">&quot;net&quot;</span><span class="p">]</span>

    <span class="k">return</span> <span class="n">checkpoint</span>


<span class="k">class</span> <span class="nc">MissingPretrainedWeightsException</span><span class="p">(</span><span class="ne">Exception</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Exception raised by unsupported pretrianed model.</span>

<span class="sd">    Attributes:</span>
<span class="sd">        message -- explanation of the error</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">desc</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">message</span> <span class="o">=</span> <span class="s2">&quot;Missing pretrained wights: &quot;</span> <span class="o">+</span> <span class="n">desc</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="bp">self</span><span class="o">.</span><span class="n">message</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">_yolox_ckpt_solver</span><span class="p">(</span><span class="n">ckpt_key</span><span class="p">,</span> <span class="n">ckpt_val</span><span class="p">,</span> <span class="n">model_key</span><span class="p">,</span> <span class="n">model_val</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Helper method for reshaping old pretrained checkpoint&#39;s focus weights to 6x6 conv weights.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">if</span> <span class="p">(</span>
        <span class="n">ckpt_val</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">model_val</span><span class="o">.</span><span class="n">shape</span>
        <span class="ow">and</span> <span class="n">ckpt_key</span> <span class="o">==</span> <span class="s2">&quot;module._backbone._modules_list.0.conv.conv.weight&quot;</span>
        <span class="ow">and</span> <span class="n">model_key</span> <span class="o">==</span> <span class="s2">&quot;_backbone._modules_list.0.conv.weight&quot;</span>
    <span class="p">):</span>
        <span class="n">model_val</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">::</span><span class="mi">2</span><span class="p">,</span> <span class="p">::</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">ckpt_val</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span>
        <span class="n">model_val</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">,</span> <span class="p">::</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">ckpt_val</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span>
        <span class="n">model_val</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">::</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">ckpt_val</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="mi">6</span><span class="p">:</span><span class="mi">9</span><span class="p">]</span>
        <span class="n">model_val</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">ckpt_val</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="mi">9</span><span class="p">:</span><span class="mi">12</span><span class="p">]</span>
        <span class="n">replacement</span> <span class="o">=</span> <span class="n">model_val</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">replacement</span> <span class="o">=</span> <span class="n">ckpt_val</span>

    <span class="k">return</span> <span class="n">replacement</span>


<span class="k">def</span> <span class="nf">load_pretrained_weights</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">architecture</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">pretrained_weights</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>

    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Loads pretrained weights from the MODEL_URLS dictionary to model</span>
<span class="sd">    @param architecture: name of the model&#39;s architecture</span>
<span class="sd">    @param model: model to load pretrinaed weights for</span>
<span class="sd">    @param pretrained_weights: name for the pretrianed weights (i.e imagenet)</span>
<span class="sd">    @return: None</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">model_url_key</span> <span class="o">=</span> <span class="n">architecture</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">pretrained_weights</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">model_url_key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">MODEL_URLS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="k">raise</span> <span class="n">MissingPretrainedWeightsException</span><span class="p">(</span><span class="n">model_url_key</span><span class="p">)</span>

    <span class="n">url</span> <span class="o">=</span> <span class="n">MODEL_URLS</span><span class="p">[</span><span class="n">model_url_key</span><span class="p">]</span>
    <span class="n">unique_filename</span> <span class="o">=</span> <span class="n">url</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;https://deci-pretrained-models.s3.amazonaws.com/&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">,</span> <span class="s2">&quot;_&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;_&quot;</span><span class="p">)</span>
    <span class="n">map_location</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
    <span class="n">pretrained_state_dict</span> <span class="o">=</span> <span class="n">load_state_dict_from_url</span><span class="p">(</span><span class="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="n">map_location</span><span class="p">,</span> <span class="n">file_name</span><span class="o">=</span><span class="n">unique_filename</span><span class="p">)</span>
    <span class="n">_load_weights</span><span class="p">(</span><span class="n">architecture</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">pretrained_state_dict</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">_load_weights</span><span class="p">(</span><span class="n">architecture</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">pretrained_state_dict</span><span class="p">):</span>
    <span class="k">if</span> <span class="s2">&quot;ema_net&quot;</span> <span class="ow">in</span> <span class="n">pretrained_state_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">pretrained_state_dict</span><span class="p">[</span><span class="s2">&quot;net&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pretrained_state_dict</span><span class="p">[</span><span class="s2">&quot;ema_net&quot;</span><span class="p">]</span>
    <span class="n">solver</span> <span class="o">=</span> <span class="n">_yolox_ckpt_solver</span> <span class="k">if</span> <span class="s2">&quot;yolox&quot;</span> <span class="ow">in</span> <span class="n">architecture</span> <span class="k">else</span> <span class="kc">None</span>
    <span class="n">adapted_pretrained_state_dict</span> <span class="o">=</span> <span class="n">adapt_state_dict_to_fit_model_layer_names</span><span class="p">(</span>
        <span class="n">model_state_dict</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">source_ckpt</span><span class="o">=</span><span class="n">pretrained_state_dict</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="n">solver</span>
    <span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">adapted_pretrained_state_dict</span><span class="p">[</span><span class="s2">&quot;net&quot;</span><span class="p">],</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">load_pretrained_weights_local</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">architecture</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">pretrained_weights</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>

    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Loads pretrained weights from the MODEL_URLS dictionary to model</span>
<span class="sd">    @param architecture: name of the model&#39;s architecture</span>
<span class="sd">    @param model: model to load pretrinaed weights for</span>
<span class="sd">    @param pretrained_weights: path tp pretrained weights</span>
<span class="sd">    @return: None</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">map_location</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>

    <span class="n">pretrained_state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">pretrained_weights</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="n">map_location</span><span class="p">)</span>
    <span class="n">_load_weights</span><span class="p">(</span><span class="n">architecture</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">pretrained_state_dict</span><span class="p">)</span>
</pre></div>

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