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<div class="section" id="conferences"> <div class="section" id="conferences">
<h2>Conferences<a class="headerlink" href="#conferences" title="Permalink to this headline"></a></h2> <h2>Conferences<a class="headerlink" href="#conferences" title="Permalink to this headline"></a></h2>
<ul class="simple"> <ul class="simple">
<li><p><strong><a class="reference external" href="#iccv-2019">ICCV 2019</a></strong></p></li> <li><strong><a class="reference external" href="#iccv-2019">ICCV 2019</a></strong></li>
<li><p><strong><a class="reference external" href="#cvpr-2019">CVPR 2019</a></strong></p></li> <li><strong><a class="reference external" href="#cvpr-2019">CVPR 2019</a></strong></li>
<li><p><strong><a class="reference external" href="#aaai-2019">AAAI 2019</a></strong></p></li> <li><strong><a class="reference external" href="#aaai-2019">AAAI 2019</a></strong></li>
<li><p><strong><a class="reference external" href="#neurips-2018">NeurIPS 2018</a></strong></p></li> <li><strong><a class="reference external" href="#neurips-2018">NeurIPS 2018</a></strong></li>
<li><p><strong><a class="reference external" href="#eccv-2018">ECCV 2018</a></strong></p></li> <li><strong><a class="reference external" href="#eccv-2018">ECCV 2018</a></strong></li>
<li><p><strong><a class="reference external" href="#cvpr-2018">CVPR 2018</a></strong></p></li> <li><strong><a class="reference external" href="#cvpr-2018">CVPR 2018</a></strong></li>
<li><p><strong><a class="reference external" href="#arxiv">ArXiv</a></strong></p></li> <li><strong><a class="reference external" href="#arxiv">ArXiv</a></strong></li>
</ul> </ul>
<div class="section" id="iccv-2019"> <div class="section" id="iccv-2019">
<h3>ICCV 2019<a class="headerlink" href="#iccv-2019" title="Permalink to this headline"></a></h3> <h3>ICCV 2019<a class="headerlink" href="#iccv-2019" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment. [<a class="reference external" href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_RGB-Infrared_Cross-Modality_Person_Re-Identification_via_Joint_Pixel_and_Feature_Alignment_ICCV_2019_paper.pdf">paper</a>] [<a class="reference external" href="https://github.com/wangguanan/AlignGAN">code</a>]</p></li> <li>RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment. [<a class="reference external" href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_RGB-Infrared_Cross-Modality_Person_Re-Identification_via_Joint_Pixel_and_Feature_Alignment_ICCV_2019_paper.pdf">paper</a>] [<a class="reference external" href="https://github.com/wangguanan/AlignGAN">code</a>]</li>
<li><p>Unsupervised Graph Association for Person Re-identification. [<a class="reference external" href="https://github.com/yichuan9527/Unsupervised-Graph-Association-for-Person-Re-identification">paper</a>] [<a class="reference external" href="https://github.com/yichuan9527/Unsupervised-Graph-Association-for-Person-Re-identification">code</a>]</p></li> <li>Unsupervised Graph Association for Person Re-identification. [<a class="reference external" href="https://github.com/yichuan9527/Unsupervised-Graph-Association-for-Person-Re-identification">paper</a>] [<a class="reference external" href="https://github.com/yichuan9527/Unsupervised-Graph-Association-for-Person-Re-identification">code</a>]</li>
<li><p>Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification. [<a class="reference external" href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Fu_Self-Similarity_Grouping_A_Simple_Unsupervised_Cross_Domain_Adaptation_Approach_for_ICCV_2019_paper.pdf">paper</a>] [<a class="reference external" href="https://github.com/OasisYang/SSG">code</a>]</p></li> <li>Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification. [<a class="reference external" href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Fu_Self-Similarity_Grouping_A_Simple_Unsupervised_Cross_Domain_Adaptation_Approach_for_ICCV_2019_paper.pdf">paper</a>] [<a class="reference external" href="https://github.com/OasisYang/SSG">code</a>]</li>
<li><p>Spectral Feature Transformation for Person Re-Identification. [<a class="reference external" href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Luo_Spectral_Feature_Transformation_for_Person_Re-Identification_ICCV_2019_paper.pdf">paper</a>] [<a class="reference external" href="https://github.com/LuckyDC/SFT_REID">code</a>]</p></li> <li>Spectral Feature Transformation for Person Re-Identification. [<a class="reference external" href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Luo_Spectral_Feature_Transformation_for_Person_Re-Identification_ICCV_2019_paper.pdf">paper</a>] [<a class="reference external" href="https://github.com/LuckyDC/SFT_REID">code</a>]</li>
<li><p>Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification. [<a class="reference external" href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Guo_Beyond_Human_Parts_Dual_Part-Aligned_Representations_for_Person_Re-Identification_ICCV_2019_paper.pdf">paper</a>] [<a class="reference external" href="https://github.com/ggjy/P2Net.pytorch">code</a>]</p></li> <li>Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification. [<a class="reference external" href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Guo_Beyond_Human_Parts_Dual_Part-Aligned_Representations_for_Person_Re-Identification_ICCV_2019_paper.pdf">paper</a>] [<a class="reference external" href="https://github.com/ggjy/P2Net.pytorch">code</a>]</li>
<li><p>Co-segmentation Inspired Attention Networks for Video-based Person Re-identification. [<a class="reference external" href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Subramaniam_Co-Segmentation_Inspired_Attention_Networks_for_Video-Based_Person_Re-Identification_ICCV_2019_paper.pdf">paper</a>][<a class="reference external" href="https://github.com/InnovArul/vidreid_cosegmentation">code</a>]</p></li> <li>Co-segmentation Inspired Attention Networks for Video-based Person Re-identification. [<a class="reference external" href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Subramaniam_Co-Segmentation_Inspired_Attention_Networks_for_Video-Based_Person_Re-Identification_ICCV_2019_paper.pdf">paper</a>][<a class="reference external" href="https://github.com/InnovArul/vidreid_cosegmentation">code</a>]</li>
<li><p>Mixed High-Order Attention Network for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1908.05819">paper</a>][<a class="reference external" href="https://github.com/chenbinghui1/MHN">code</a>]</p></li> <li>Mixed High-Order Attention Network for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1908.05819">paper</a>][<a class="reference external" href="https://github.com/chenbinghui1/MHN">code</a>]</li>
<li><p>ABD-Net: Attentive but Diverse Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1908.01114">paper</a>] [<a class="reference external" href="https://github.com/TAMU-VITA/ABD-Net">code</a>]</p></li> <li>ABD-Net: Attentive but Diverse Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1908.01114">paper</a>] [<a class="reference external" href="https://github.com/TAMU-VITA/ABD-Net">code</a>]</li>
<li><p>Omni-Scale Feature Learning for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1905.00953">paper</a>] [<a class="reference external" href="https://github.com/KaiyangZhou/deep-person-reid">code</a>]</p></li> <li>Omni-Scale Feature Learning for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1905.00953">paper</a>] [<a class="reference external" href="https://github.com/KaiyangZhou/deep-person-reid">code</a>]</li>
</ul> </ul>
</div> </div>
<div class="section" id="cvpr-2019"> <div class="section" id="cvpr-2019">
<h3>CVPR 2019<a class="headerlink" href="#cvpr-2019" title="Permalink to this headline"></a></h3> <h3>CVPR 2019<a class="headerlink" href="#cvpr-2019" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Joint Discriminative and Generative Learning for Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1904.07223">paper</a>][<a class="reference external" href="https://github.com/NVlabs/DG-Net">code</a>]</p></li> <li>Joint Discriminative and Generative Learning for Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1904.07223">paper</a>][<a class="reference external" href="https://github.com/NVlabs/DG-Net">code</a>]</li>
<li><p>Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1904.01990">paper</a>][<a class="reference external" href="https://github.com/zhunzhong07/ECN">code</a>]</p></li> <li>Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1904.01990">paper</a>][<a class="reference external" href="https://github.com/zhunzhong07/ECN">code</a>]</li>
<li><p>Dissecting Person Re-identification from the Viewpoint of Viewpoint. [<a class="reference external" href="https://arxiv.org/abs/1812.02162">paper</a>][<a class="reference external" href="https://github.com/sxzrt/Dissecting-Person-Re-ID-from-the-Viewpoint-of-Viewpoint">code</a>]</p></li> <li>Dissecting Person Re-identification from the Viewpoint of Viewpoint. [<a class="reference external" href="https://arxiv.org/abs/1812.02162">paper</a>][<a class="reference external" href="https://github.com/sxzrt/Dissecting-Person-Re-ID-from-the-Viewpoint-of-Viewpoint">code</a>]</li>
<li><p>Unsupervised Person Re-identification by Soft Multilabel Learning. [<a class="reference external" href="https://arxiv.org/abs/1903.06325">paper</a>][<a class="reference external" href="https://github.com/KovenYu/MAR">code</a>]</p></li> <li>Unsupervised Person Re-identification by Soft Multilabel Learning. [<a class="reference external" href="https://arxiv.org/abs/1903.06325">paper</a>][<a class="reference external" href="https://github.com/KovenYu/MAR">code</a>]</li>
<li><p>Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification. [<a class="reference external" href="https://kovenyu.com/publication/2019-cvpr-pedal/">paper</a>][<a class="reference external" href="https://github.com/QizeYang/PAUL">code</a>]</p></li> <li>Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification. [<a class="reference external" href="https://kovenyu.com/publication/2019-cvpr-pedal/">paper</a>][<a class="reference external" href="https://github.com/QizeYang/PAUL">code</a>]</li>
</ul> </ul>
</div> </div>
<div class="section" id="aaai-2019"> <div class="section" id="aaai-2019">
<h3>AAAI 2019<a class="headerlink" href="#aaai-2019" title="Permalink to this headline"></a></h3> <h3>AAAI 2019<a class="headerlink" href="#aaai-2019" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Spatial and Temporal Mutual Promotion for Video-based Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1812.10305">paper</a>][<a class="reference external" href="https://github.com/yolomax/person-reid-lib">code</a>]</p></li> <li>Spatial and Temporal Mutual Promotion for Video-based Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1812.10305">paper</a>][<a class="reference external" href="https://github.com/yolomax/person-reid-lib">code</a>]</li>
<li><p>Spatial-Temporal Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1812.03282">paper</a>][<a class="reference external" href="https://github.com/Wanggcong/Spatial-Temporal-Re-identification">code</a>]</p></li> <li>Spatial-Temporal Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1812.03282">paper</a>][<a class="reference external" href="https://github.com/Wanggcong/Spatial-Temporal-Re-identification">code</a>]</li>
<li><p>Horizontal Pyramid Matching for Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1804.05275">paper</a>][<a class="reference external" href="https://github.com/OasisYang/HPM">code</a>]</p></li> <li>Horizontal Pyramid Matching for Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1804.05275">paper</a>][<a class="reference external" href="https://github.com/OasisYang/HPM">code</a>]</li>
<li><p>Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1901.06140">paper</a>][<a class="reference external" href="https://github.com/youngminPIL/rollback">code</a>]</p></li> <li>Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1901.06140">paper</a>][<a class="reference external" href="https://github.com/youngminPIL/rollback">code</a>]</li>
<li><p>A Bottom-Up Clustering Approach to Unsupervised Person Re-identification. [<a class="reference external" href="https://vana77.github.io/vana77.github.io/images/AAAI19.pdf">paper</a>][<a class="reference external" href="https://github.com/vana77/Bottom-up-Clustering-Person-Re-identification">code</a>]</p></li> <li>A Bottom-Up Clustering Approach to Unsupervised Person Re-identification. [<a class="reference external" href="https://vana77.github.io/vana77.github.io/images/AAAI19.pdf">paper</a>][<a class="reference external" href="https://github.com/vana77/Bottom-up-Clustering-Person-Re-identification">code</a>]</li>
</ul> </ul>
</div> </div>
<div class="section" id="neurips-2018"> <div class="section" id="neurips-2018">
<h3>NeurIPS 2018<a class="headerlink" href="#neurips-2018" title="Permalink to this headline"></a></h3> <h3>NeurIPS 2018<a class="headerlink" href="#neurips-2018" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1810.02936">paper</a>][<a class="reference external" href="https://github.com/yxgeee/FD-GAN">code</a>]</p></li> <li>FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1810.02936">paper</a>][<a class="reference external" href="https://github.com/yxgeee/FD-GAN">code</a>]</li>
</ul> </ul>
</div> </div>
<div class="section" id="eccv-2018"> <div class="section" id="eccv-2018">
<h3>ECCV 2018<a class="headerlink" href="#eccv-2018" title="Permalink to this headline"></a></h3> <h3>ECCV 2018<a class="headerlink" href="#eccv-2018" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Generalizing A Person Retrieval Model Hetero- and Homogeneously. [<a class="reference external" href="http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhun_Zhong_Generalizing_A_Person_ECCV_2018_paper.pdf">paper</a>][<a class="reference external" href="https://github.com/zhunzhong07/HHL">code</a>]</p></li> <li>Generalizing A Person Retrieval Model Hetero- and Homogeneously. [<a class="reference external" href="http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhun_Zhong_Generalizing_A_Person_ECCV_2018_paper.pdf">paper</a>][<a class="reference external" href="https://github.com/zhunzhong07/HHL">code</a>]</li>
<li><p>Pose-Normalized Image Generation for Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1712.02225">paper</a>][<a class="reference external" href="https://github.com/naiq/PN_GAN">code</a>]</p></li> <li>Pose-Normalized Image Generation for Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1712.02225">paper</a>][<a class="reference external" href="https://github.com/naiq/PN_GAN">code</a>]</li>
</ul> </ul>
</div> </div>
<div class="section" id="cvpr-2018"> <div class="section" id="cvpr-2018">
<h3>CVPR 2018<a class="headerlink" href="#cvpr-2018" title="Permalink to this headline"></a></h3> <h3>CVPR 2018<a class="headerlink" href="#cvpr-2018" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Camera Style Adaptation for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1711.10295">paper</a>][<a class="reference external" href="https://github.com/zhunzhong07/CamStyle">code</a>]</p></li> <li>Camera Style Adaptation for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1711.10295">paper</a>][<a class="reference external" href="https://github.com/zhunzhong07/CamStyle">code</a>]</li>
<li><p>Deep Group-Shuffling Random Walk for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1807.11178">paper</a>][<a class="reference external" href="https://github.com/YantaoShen/kpm_rw_person_reid">code</a>]</p></li> <li>Deep Group-Shuffling Random Walk for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1807.11178">paper</a>][<a class="reference external" href="https://github.com/YantaoShen/kpm_rw_person_reid">code</a>]</li>
<li><p>End-to-End Deep Kronecker-Product Matching for Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1807.11182">paper</a>][<a class="reference external" href="https://github.com/YantaoShen/kpm_rw_person_reid">code</a>]</p></li> <li>End-to-End Deep Kronecker-Product Matching for Person Re-identification. [<a class="reference external" href="https://arxiv.org/abs/1807.11182">paper</a>][<a class="reference external" href="https://github.com/YantaoShen/kpm_rw_person_reid">code</a>]</li>
<li><p>Features for Multi-Target Multi-Camera Tracking and Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1803.10859">paper</a>][<a class="reference external" href="https://github.com/ergysr/DeepCC">code</a>]</p></li> <li>Features for Multi-Target Multi-Camera Tracking and Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1803.10859">paper</a>][<a class="reference external" href="https://github.com/ergysr/DeepCC">code</a>]</li>
<li><p>Group Consistent Similarity Learning via Deep CRF for Person Re-Identification. [<a class="reference external" href="http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Group_Consistent_Similarity_CVPR_2018_paper.pdf">paper</a>][<a class="reference external" href="https://github.com/dapengchen123/crf_affinity">code</a>]</p></li> <li>Group Consistent Similarity Learning via Deep CRF for Person Re-Identification. [<a class="reference external" href="http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Group_Consistent_Similarity_CVPR_2018_paper.pdf">paper</a>][<a class="reference external" href="https://github.com/dapengchen123/crf_affinity">code</a>]</li>
<li><p>Harmonious Attention Network for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1802.08122">paper</a>][<a class="reference external" href="https://github.com/KaiyangZhou/deep-person-reid">code</a>]</p></li> <li>Harmonious Attention Network for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1802.08122">paper</a>][<a class="reference external" href="https://github.com/KaiyangZhou/deep-person-reid">code</a>]</li>
<li><p>Human Semantic Parsing for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1804.00216">paper</a>][<a class="reference external" href="https://github.com/emrahbasaran/SPReID">code</a>]</p></li> <li>Human Semantic Parsing for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1804.00216">paper</a>][<a class="reference external" href="https://github.com/emrahbasaran/SPReID">code</a>]</li>
<li><p>Multi-Level Factorisation Net for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1803.09132">paper</a>][<a class="reference external" href="https://github.com/KaiyangZhou/deep-person-reid">code</a>]</p></li> <li>Multi-Level Factorisation Net for Person Re-Identification. [<a class="reference external" href="https://arxiv.org/abs/1803.09132">paper</a>][<a class="reference external" href="https://github.com/KaiyangZhou/deep-person-reid">code</a>]</li>
<li><p>Resource Aware Person Re-identification across Multiple Resolutions. [<a class="reference external" href="https://arxiv.org/abs/1805.08805">paper</a>][<a class="reference external" href="https://github.com/mileyan/DARENet">code</a>]</p></li> <li>Resource Aware Person Re-identification across Multiple Resolutions. [<a class="reference external" href="https://arxiv.org/abs/1805.08805">paper</a>][<a class="reference external" href="https://github.com/mileyan/DARENet">code</a>]</li>
<li><p>Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning. [<a class="reference external" href="https://yu-wu.net/pdf/CVPR2018_Exploit-Unknown-Gradually.pdf">paper</a>][<a class="reference external" href="https://github.com/Yu-Wu/Exploit-Unknown-Gradually">code</a>]</p></li> <li>Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning. [<a class="reference external" href="https://yu-wu.net/pdf/CVPR2018_Exploit-Unknown-Gradually.pdf">paper</a>][<a class="reference external" href="https://github.com/Yu-Wu/Exploit-Unknown-Gradually">code</a>]</li>
</ul> </ul>
</div> </div>
<div class="section" id="arxiv"> <div class="section" id="arxiv">
<h3>ArXiv<a class="headerlink" href="#arxiv" title="Permalink to this headline"></a></h3> <h3>ArXiv<a class="headerlink" href="#arxiv" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Revisiting Temporal Modeling for Video-based Person ReID. [<a class="reference external" href="https://arxiv.org/abs/1805.02104">paper</a>][<a class="reference external" href="https://github.com/jiyanggao/Video-Person-ReID">code</a>]</p></li> <li>Revisiting Temporal Modeling for Video-based Person ReID. [<a class="reference external" href="https://arxiv.org/abs/1805.02104">paper</a>][<a class="reference external" href="https://github.com/jiyanggao/Video-Person-ReID">code</a>]</li>
<li><p>Learning Generalisable Omni-Scale Representations for Person Re-Identification [<a class="reference external" href="https://arxiv.org/abs/1910.06827">paper</a>][<a class="reference external" href="https://github.com/KaiyangZhou/deep-person-reid">code</a>]</p></li> <li>Learning Generalisable Omni-Scale Representations for Person Re-Identification [<a class="reference external" href="https://arxiv.org/abs/1910.06827">paper</a>][<a class="reference external" href="https://github.com/KaiyangZhou/deep-person-reid">code</a>]</li>
</ul> </ul>
</div> </div>
</div> </div>

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<div class="section" id="model-zoo"> <div class="section" id="model-zoo">
<h1>Model Zoo<a class="headerlink" href="#model-zoo" title="Permalink to this headline"></a></h1> <h1>Model Zoo<a class="headerlink" href="#model-zoo" title="Permalink to this headline"></a></h1>
<ul class="simple"> <ul class="simple">
<li><p>Results are presented in the format of <em>&lt;Rank-1 (mAP)&gt;</em>.</p></li> <li>Results are presented in the format of <em>&lt;Rank-1 (mAP)&gt;</em>.</li>
<li><p>When computing model size and FLOPs, only layers that are used at test time are considered (see <code class="docutils literal notranslate"><span class="pre">torchreid.utils.compute_model_complexity</span></code>).</p></li> <li>When computing model size and FLOPs, only layers that are used at test time are considered (see <code class="docutils literal notranslate"><span class="pre">torchreid.utils.compute_model_complexity</span></code>).</li>
<li><p>Asterisk (*) means the model is trained from scratch.</p></li> <li>Asterisk (*) means the model is trained from scratch.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">combineall=True</span></code> means all images in the dataset are used for model training.</p></li> <li><code class="docutils literal notranslate"><span class="pre">combineall=True</span></code> means all images in the dataset are used for model training.</li>
</ul> </ul>
<div class="section" id="imagenet-pretrained-models"> <div class="section" id="imagenet-pretrained-models">
<h2>ImageNet pretrained models<a class="headerlink" href="#imagenet-pretrained-models" title="Permalink to this headline"></a></h2> <h2>ImageNet pretrained models<a class="headerlink" href="#imagenet-pretrained-models" title="Permalink to this headline"></a></h2>

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_static/basic.css
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@ -231,16 +231,6 @@ a.headerlink {
visibility: hidden; visibility: hidden;
} }
a.brackets:before,
span.brackets > a:before{
content: "[";
}
a.brackets:after,
span.brackets > a:after {
content: "]";
}
h1:hover > a.headerlink, h1:hover > a.headerlink,
h2:hover > a.headerlink, h2:hover > a.headerlink,
h3:hover > a.headerlink, h3:hover > a.headerlink,
@ -289,12 +279,6 @@ img.align-center, .figure.align-center, object.align-center {
margin-right: auto; margin-right: auto;
} }
img.align-default, .figure.align-default {
display: block;
margin-left: auto;
margin-right: auto;
}
.align-left { .align-left {
text-align: left; text-align: left;
} }
@ -303,10 +287,6 @@ img.align-default, .figure.align-default {
text-align: center; text-align: center;
} }
.align-default {
text-align: center;
}
.align-right { .align-right {
text-align: right; text-align: right;
} }
@ -378,11 +358,6 @@ table.align-center {
margin-right: auto; margin-right: auto;
} }
table.align-default {
margin-left: auto;
margin-right: auto;
}
table caption span.caption-number { table caption span.caption-number {
font-style: italic; font-style: italic;
} }
@ -416,16 +391,6 @@ table.citation td {
border-bottom: none; border-bottom: none;
} }
th > p:first-child,
td > p:first-child {
margin-top: 0px;
}
th > p:last-child,
td > p:last-child {
margin-bottom: 0px;
}
/* -- figures --------------------------------------------------------------- */ /* -- figures --------------------------------------------------------------- */
div.figure { div.figure {
@ -495,58 +460,11 @@ ol.upperroman {
list-style: upper-roman; list-style: upper-roman;
} }
li > p:first-child {
margin-top: 0px;
}
li > p:last-child {
margin-bottom: 0px;
}
dl.footnote > dt,
dl.citation > dt {
float: left;
}
dl.footnote > dd,
dl.citation > dd {
margin-bottom: 0em;
}
dl.footnote > dd:after,
dl.citation > dd:after {
content: "";
clear: both;
}
dl.field-list {
display: grid;
grid-template-columns: fit-content(30%) auto;
}
dl.field-list > dt {
font-weight: bold;
word-break: break-word;
padding-left: 0.5em;
padding-right: 5px;
}
dl.field-list > dt:after {
content: ":";
}
dl.field-list > dd {
padding-left: 0.5em;
margin-top: 0em;
margin-left: 0em;
margin-bottom: 0em;
}
dl { dl {
margin-bottom: 15px; margin-bottom: 15px;
} }
dd > p:first-child { dd p {
margin-top: 0px; margin-top: 0px;
} }
@ -619,12 +537,6 @@ dl.glossary dt {
font-style: oblique; font-style: oblique;
} }
.classifier:before {
font-style: normal;
margin: 0.5em;
content: ":";
}
abbr, acronym { abbr, acronym {
border-bottom: dotted 1px; border-bottom: dotted 1px;
cursor: help; cursor: help;

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@ -87,13 +87,14 @@ jQuery.fn.highlightText = function(text, className) {
node.nextSibling)); node.nextSibling));
node.nodeValue = val.substr(0, pos); node.nodeValue = val.substr(0, pos);
if (isInSVG) { if (isInSVG) {
var bbox = span.getBBox();
var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
var bbox = node.parentElement.getBBox(); rect.x.baseVal.value = bbox.x;
rect.x.baseVal.value = bbox.x;
rect.y.baseVal.value = bbox.y; rect.y.baseVal.value = bbox.y;
rect.width.baseVal.value = bbox.width; rect.width.baseVal.value = bbox.width;
rect.height.baseVal.value = bbox.height; rect.height.baseVal.value = bbox.height;
rect.setAttribute('class', className); rect.setAttribute('class', className);
var parentOfText = node.parentNode.parentNode;
addItems.push({ addItems.push({
"parent": node.parentNode, "parent": node.parentNode,
"target": rect}); "target": rect});

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@ -6,5 +6,5 @@ var DOCUMENTATION_OPTIONS = {
FILE_SUFFIX: '.html', FILE_SUFFIX: '.html',
HAS_SOURCE: true, HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt', SOURCELINK_SUFFIX: '.txt',
NAVIGATION_WITH_KEYS: false NAVIGATION_WITH_KEYS: false,
}; };

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@ -36,10 +36,8 @@ if (!Scorer) {
// query found in title // query found in title
title: 15, title: 15,
partialTitle: 7,
// query found in terms // query found in terms
term: 5, term: 5
partialTerm: 2
}; };
} }
@ -58,14 +56,6 @@ var Search = {
_queued_query : null, _queued_query : null,
_pulse_status : -1, _pulse_status : -1,
htmlToText : function(htmlString) {
var htmlElement = document.createElement('span');
htmlElement.innerHTML = htmlString;
$(htmlElement).find('.headerlink').remove();
docContent = $(htmlElement).find('[role=main]')[0];
return docContent.textContent || docContent.innerText;
},
init : function() { init : function() {
var params = $.getQueryParameters(); var params = $.getQueryParameters();
if (params.q) { if (params.q) {
@ -130,7 +120,7 @@ var Search = {
this.out = $('#search-results'); this.out = $('#search-results');
this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out); this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
this.dots = $('<span></span>').appendTo(this.title); this.dots = $('<span></span>').appendTo(this.title);
this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out); this.status = $('<p style="display: none"></p>').appendTo(this.out);
this.output = $('<ul class="search"/>').appendTo(this.out); this.output = $('<ul class="search"/>').appendTo(this.out);
$('#search-progress').text(_('Preparing search...')); $('#search-progress').text(_('Preparing search...'));
@ -269,7 +259,11 @@ var Search = {
displayNextItem(); displayNextItem();
}); });
} else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) { } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
$.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX, var suffix = DOCUMENTATION_OPTIONS.SOURCELINK_SUFFIX;
if (suffix === undefined) {
suffix = '.txt';
}
$.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[5] + (item[5].slice(-suffix.length) === suffix ? '' : suffix),
dataType: "text", dataType: "text",
complete: function(jqxhr, textstatus) { complete: function(jqxhr, textstatus) {
var data = jqxhr.responseText; var data = jqxhr.responseText;
@ -319,13 +313,12 @@ var Search = {
for (var prefix in objects) { for (var prefix in objects) {
for (var name in objects[prefix]) { for (var name in objects[prefix]) {
var fullname = (prefix ? prefix + '.' : '') + name; var fullname = (prefix ? prefix + '.' : '') + name;
var fullnameLower = fullname.toLowerCase() if (fullname.toLowerCase().indexOf(object) > -1) {
if (fullnameLower.indexOf(object) > -1) {
var score = 0; var score = 0;
var parts = fullnameLower.split('.'); var parts = fullname.split('.');
// check for different match types: exact matches of full name or // check for different match types: exact matches of full name or
// "last name" (i.e. last dotted part) // "last name" (i.e. last dotted part)
if (fullnameLower == object || parts[parts.length - 1] == object) { if (fullname == object || parts[parts.length - 1] == object) {
score += Scorer.objNameMatch; score += Scorer.objNameMatch;
// matches in last name // matches in last name
} else if (parts[parts.length - 1].indexOf(object) > -1) { } else if (parts[parts.length - 1].indexOf(object) > -1) {
@ -392,19 +385,6 @@ var Search = {
{files: terms[word], score: Scorer.term}, {files: terms[word], score: Scorer.term},
{files: titleterms[word], score: Scorer.title} {files: titleterms[word], score: Scorer.title}
]; ];
// add support for partial matches
if (word.length > 2) {
for (var w in terms) {
if (w.match(word) && !terms[word]) {
_o.push({files: terms[w], score: Scorer.partialTerm})
}
}
for (var w in titleterms) {
if (w.match(word) && !titleterms[word]) {
_o.push({files: titleterms[w], score: Scorer.partialTitle})
}
}
}
// no match but word was a required one // no match but word was a required one
if ($u.every(_o, function(o){return o.files === undefined;})) { if ($u.every(_o, function(o){return o.files === undefined;})) {
@ -444,12 +424,8 @@ var Search = {
var valid = true; var valid = true;
// check if all requirements are matched // check if all requirements are matched
var filteredTermCount = // as search terms with length < 3 are discarded: ignore if (fileMap[file].length != searchterms.length)
searchterms.filter(function(term){return term.length > 2}).length continue;
if (
fileMap[file].length != searchterms.length &&
fileMap[file].length != filteredTermCount
) continue;
// ensure that none of the excluded terms is in the search result // ensure that none of the excluded terms is in the search result
for (i = 0; i < excluded.length; i++) { for (i = 0; i < excluded.length; i++) {
@ -480,8 +456,7 @@ var Search = {
* words. the first one is used to find the occurrence, the * words. the first one is used to find the occurrence, the
* latter for highlighting it. * latter for highlighting it.
*/ */
makeSearchSummary : function(htmlText, keywords, hlwords) { makeSearchSummary : function(text, keywords, hlwords) {
var text = Search.htmlToText(htmlText);
var textLower = text.toLowerCase(); var textLower = text.toLowerCase();
var start = 0; var start = 0;
$.each(keywords, function() { $.each(keywords, function() {

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@ -0,0 +1,808 @@
/*
* websupport.js
* ~~~~~~~~~~~~~
*
* sphinx.websupport utilities for all documentation.
*
* :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
(function($) {
$.fn.autogrow = function() {
return this.each(function() {
var textarea = this;
$.fn.autogrow.resize(textarea);
$(textarea)
.focus(function() {
textarea.interval = setInterval(function() {
$.fn.autogrow.resize(textarea);
}, 500);
})
.blur(function() {
clearInterval(textarea.interval);
});
});
};
$.fn.autogrow.resize = function(textarea) {
var lineHeight = parseInt($(textarea).css('line-height'), 10);
var lines = textarea.value.split('\n');
var columns = textarea.cols;
var lineCount = 0;
$.each(lines, function() {
lineCount += Math.ceil(this.length / columns) || 1;
});
var height = lineHeight * (lineCount + 1);
$(textarea).css('height', height);
};
})(jQuery);
(function($) {
var comp, by;
function init() {
initEvents();
initComparator();
}
function initEvents() {
$(document).on("click", 'a.comment-close', function(event) {
event.preventDefault();
hide($(this).attr('id').substring(2));
});
$(document).on("click", 'a.vote', function(event) {
event.preventDefault();
handleVote($(this));
});
$(document).on("click", 'a.reply', function(event) {
event.preventDefault();
openReply($(this).attr('id').substring(2));
});
$(document).on("click", 'a.close-reply', function(event) {
event.preventDefault();
closeReply($(this).attr('id').substring(2));
});
$(document).on("click", 'a.sort-option', function(event) {
event.preventDefault();
handleReSort($(this));
});
$(document).on("click", 'a.show-proposal', function(event) {
event.preventDefault();
showProposal($(this).attr('id').substring(2));
});
$(document).on("click", 'a.hide-proposal', function(event) {
event.preventDefault();
hideProposal($(this).attr('id').substring(2));
});
$(document).on("click", 'a.show-propose-change', function(event) {
event.preventDefault();
showProposeChange($(this).attr('id').substring(2));
});
$(document).on("click", 'a.hide-propose-change', function(event) {
event.preventDefault();
hideProposeChange($(this).attr('id').substring(2));
});
$(document).on("click", 'a.accept-comment', function(event) {
event.preventDefault();
acceptComment($(this).attr('id').substring(2));
});
$(document).on("click", 'a.delete-comment', function(event) {
event.preventDefault();
deleteComment($(this).attr('id').substring(2));
});
$(document).on("click", 'a.comment-markup', function(event) {
event.preventDefault();
toggleCommentMarkupBox($(this).attr('id').substring(2));
});
}
/**
* Set comp, which is a comparator function used for sorting and
* inserting comments into the list.
*/
function setComparator() {
// If the first three letters are "asc", sort in ascending order
// and remove the prefix.
if (by.substring(0,3) == 'asc') {
var i = by.substring(3);
comp = function(a, b) { return a[i] - b[i]; };
} else {
// Otherwise sort in descending order.
comp = function(a, b) { return b[by] - a[by]; };
}
// Reset link styles and format the selected sort option.
$('a.sel').attr('href', '#').removeClass('sel');
$('a.by' + by).removeAttr('href').addClass('sel');
}
/**
* Create a comp function. If the user has preferences stored in
* the sortBy cookie, use those, otherwise use the default.
*/
function initComparator() {
by = 'rating'; // Default to sort by rating.
// If the sortBy cookie is set, use that instead.
if (document.cookie.length > 0) {
var start = document.cookie.indexOf('sortBy=');
if (start != -1) {
start = start + 7;
var end = document.cookie.indexOf(";", start);
if (end == -1) {
end = document.cookie.length;
by = unescape(document.cookie.substring(start, end));
}
}
}
setComparator();
}
/**
* Show a comment div.
*/
function show(id) {
$('#ao' + id).hide();
$('#ah' + id).show();
var context = $.extend({id: id}, opts);
var popup = $(renderTemplate(popupTemplate, context)).hide();
popup.find('textarea[name="proposal"]').hide();
popup.find('a.by' + by).addClass('sel');
var form = popup.find('#cf' + id);
form.submit(function(event) {
event.preventDefault();
addComment(form);
});
$('#s' + id).after(popup);
popup.slideDown('fast', function() {
getComments(id);
});
}
/**
* Hide a comment div.
*/
function hide(id) {
$('#ah' + id).hide();
$('#ao' + id).show();
var div = $('#sc' + id);
div.slideUp('fast', function() {
div.remove();
});
}
/**
* Perform an ajax request to get comments for a node
* and insert the comments into the comments tree.
*/
function getComments(id) {
$.ajax({
type: 'GET',
url: opts.getCommentsURL,
data: {node: id},
success: function(data, textStatus, request) {
var ul = $('#cl' + id);
var speed = 100;
$('#cf' + id)
.find('textarea[name="proposal"]')
.data('source', data.source);
if (data.comments.length === 0) {
ul.html('<li>No comments yet.</li>');
ul.data('empty', true);
} else {
// If there are comments, sort them and put them in the list.
var comments = sortComments(data.comments);
speed = data.comments.length * 100;
appendComments(comments, ul);
ul.data('empty', false);
}
$('#cn' + id).slideUp(speed + 200);
ul.slideDown(speed);
},
error: function(request, textStatus, error) {
showError('Oops, there was a problem retrieving the comments.');
},
dataType: 'json'
});
}
/**
* Add a comment via ajax and insert the comment into the comment tree.
*/
function addComment(form) {
var node_id = form.find('input[name="node"]').val();
var parent_id = form.find('input[name="parent"]').val();
var text = form.find('textarea[name="comment"]').val();
var proposal = form.find('textarea[name="proposal"]').val();
if (text == '') {
showError('Please enter a comment.');
return;
}
// Disable the form that is being submitted.
form.find('textarea,input').attr('disabled', 'disabled');
// Send the comment to the server.
$.ajax({
type: "POST",
url: opts.addCommentURL,
dataType: 'json',
data: {
node: node_id,
parent: parent_id,
text: text,
proposal: proposal
},
success: function(data, textStatus, error) {
// Reset the form.
if (node_id) {
hideProposeChange(node_id);
}
form.find('textarea')
.val('')
.add(form.find('input'))
.removeAttr('disabled');
var ul = $('#cl' + (node_id || parent_id));
if (ul.data('empty')) {
$(ul).empty();
ul.data('empty', false);
}
insertComment(data.comment);
var ao = $('#ao' + node_id);
ao.find('img').attr({'src': opts.commentBrightImage});
if (node_id) {
// if this was a "root" comment, remove the commenting box
// (the user can get it back by reopening the comment popup)
$('#ca' + node_id).slideUp();
}
},
error: function(request, textStatus, error) {
form.find('textarea,input').removeAttr('disabled');
showError('Oops, there was a problem adding the comment.');
}
});
}
/**
* Recursively append comments to the main comment list and children
* lists, creating the comment tree.
*/
function appendComments(comments, ul) {
$.each(comments, function() {
var div = createCommentDiv(this);
ul.append($(document.createElement('li')).html(div));
appendComments(this.children, div.find('ul.comment-children'));
// To avoid stagnating data, don't store the comments children in data.
this.children = null;
div.data('comment', this);
});
}
/**
* After adding a new comment, it must be inserted in the correct
* location in the comment tree.
*/
function insertComment(comment) {
var div = createCommentDiv(comment);
// To avoid stagnating data, don't store the comments children in data.
comment.children = null;
div.data('comment', comment);
var ul = $('#cl' + (comment.node || comment.parent));
var siblings = getChildren(ul);
var li = $(document.createElement('li'));
li.hide();
// Determine where in the parents children list to insert this comment.
for(var i=0; i < siblings.length; i++) {
if (comp(comment, siblings[i]) <= 0) {
$('#cd' + siblings[i].id)
.parent()
.before(li.html(div));
li.slideDown('fast');
return;
}
}
// If we get here, this comment rates lower than all the others,
// or it is the only comment in the list.
ul.append(li.html(div));
li.slideDown('fast');
}
function acceptComment(id) {
$.ajax({
type: 'POST',
url: opts.acceptCommentURL,
data: {id: id},
success: function(data, textStatus, request) {
$('#cm' + id).fadeOut('fast');
$('#cd' + id).removeClass('moderate');
},
error: function(request, textStatus, error) {
showError('Oops, there was a problem accepting the comment.');
}
});
}
function deleteComment(id) {
$.ajax({
type: 'POST',
url: opts.deleteCommentURL,
data: {id: id},
success: function(data, textStatus, request) {
var div = $('#cd' + id);
if (data == 'delete') {
// Moderator mode: remove the comment and all children immediately
div.slideUp('fast', function() {
div.remove();
});
return;
}
// User mode: only mark the comment as deleted
div
.find('span.user-id:first')
.text('[deleted]').end()
.find('div.comment-text:first')
.text('[deleted]').end()
.find('#cm' + id + ', #dc' + id + ', #ac' + id + ', #rc' + id +
', #sp' + id + ', #hp' + id + ', #cr' + id + ', #rl' + id)
.remove();
var comment = div.data('comment');
comment.username = '[deleted]';
comment.text = '[deleted]';
div.data('comment', comment);
},
error: function(request, textStatus, error) {
showError('Oops, there was a problem deleting the comment.');
}
});
}
function showProposal(id) {
$('#sp' + id).hide();
$('#hp' + id).show();
$('#pr' + id).slideDown('fast');
}
function hideProposal(id) {
$('#hp' + id).hide();
$('#sp' + id).show();
$('#pr' + id).slideUp('fast');
}
function showProposeChange(id) {
$('#pc' + id).hide();
$('#hc' + id).show();
var textarea = $('#pt' + id);
textarea.val(textarea.data('source'));
$.fn.autogrow.resize(textarea[0]);
textarea.slideDown('fast');
}
function hideProposeChange(id) {
$('#hc' + id).hide();
$('#pc' + id).show();
var textarea = $('#pt' + id);
textarea.val('').removeAttr('disabled');
textarea.slideUp('fast');
}
function toggleCommentMarkupBox(id) {
$('#mb' + id).toggle();
}
/** Handle when the user clicks on a sort by link. */
function handleReSort(link) {
var classes = link.attr('class').split(/\s+/);
for (var i=0; i<classes.length; i++) {
if (classes[i] != 'sort-option') {
by = classes[i].substring(2);
}
}
setComparator();
// Save/update the sortBy cookie.
var expiration = new Date();
expiration.setDate(expiration.getDate() + 365);
document.cookie= 'sortBy=' + escape(by) +
';expires=' + expiration.toUTCString();
$('ul.comment-ul').each(function(index, ul) {
var comments = getChildren($(ul), true);
comments = sortComments(comments);
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150
datasets.html
View File

@ -197,40 +197,38 @@
<p>Suppose you wanna store the reid data in a directory called “path/to/reid-data/”, you need to specify the <code class="docutils literal notranslate"><span class="pre">root</span></code> as <em>root=’path/to/reid-data/’</em> when initializing <code class="docutils literal notranslate"><span class="pre">DataManager</span></code>. Below we use <code class="docutils literal notranslate"><span class="pre">$REID</span></code> to denote “path/to/reid-data”.</p> <p>Suppose you wanna store the reid data in a directory called “path/to/reid-data/”, you need to specify the <code class="docutils literal notranslate"><span class="pre">root</span></code> as <em>root=’path/to/reid-data/’</em> when initializing <code class="docutils literal notranslate"><span class="pre">DataManager</span></code>. Below we use <code class="docutils literal notranslate"><span class="pre">$REID</span></code> to denote “path/to/reid-data”.</p>
<p>Please refer to <a class="reference internal" href="pkg/data.html#torchreid-data"><span class="std std-ref">torchreid.data</span></a> for details regarding the arguments.</p> <p>Please refer to <a class="reference internal" href="pkg/data.html#torchreid-data"><span class="std std-ref">torchreid.data</span></a> for details regarding the arguments.</p>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>Dataset with a <span class="math notranslate nohighlight">\(\dagger\)</span> superscript means that the process is automated, so you can directly call the dataset in <code class="docutils literal notranslate"><span class="pre">DataManager</span></code> (which automatically downloads the dataset and organizes the data structure). However, we also provide a way below to help the manual setup in case the automation fails.</p> <p class="last">Dataset with a <span class="math notranslate nohighlight">\(\dagger\)</span> superscript means that the process is automated, so you can directly call the dataset in <code class="docutils literal notranslate"><span class="pre">DataManager</span></code> (which automatically downloads the dataset and organizes the data structure). However, we also provide a way below to help the manual setup in case the automation fails.</p>
</div> </div>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>The keys to use specific datasets are enclosed in the parantheses beside the datasets.</p> <p class="last">The keys to use specific datasets are enclosed in the parantheses beside the datasets.</p>
</div> </div>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>You are suggested to use the provided names for dataset folders such as “market1501” for Market1501 and “dukemtmcreid” for DukeMTMC-reID when doing the manual setup, otherwise you need to modify the source code accordingly (i.e. the <code class="docutils literal notranslate"><span class="pre">dataset_dir</span></code> attribute).</p> <p class="last">You are suggested to use the provided names for dataset folders such as “market1501” for Market1501 and “dukemtmcreid” for DukeMTMC-reID when doing the manual setup, otherwise you need to modify the source code accordingly (i.e. the <code class="docutils literal notranslate"><span class="pre">dataset_dir</span></code> attribute).</p>
</div> </div>
<div class="contents local topic" id="contents"> <div class="contents local topic" id="contents">
<ul class="simple"> <ul class="simple">
<li><p><a class="reference internal" href="#image-datasets" id="id1">Image Datasets</a></p> <li><a class="reference internal" href="#image-datasets" id="id1">Image Datasets</a><ul>
<ul> <li><a class="reference internal" href="#market1501-dagger-market1501" id="id2">Market1501 <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">market1501</span></code>)</a></li>
<li><p><a class="reference internal" href="#market1501-dagger-market1501" id="id2">Market1501 <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">market1501</span></code>)</a></p></li> <li><a class="reference internal" href="#cuhk03-cuhk03" id="id3">CUHK03 (<code class="docutils literal notranslate"><span class="pre">cuhk03</span></code>)</a></li>
<li><p><a class="reference internal" href="#cuhk03-cuhk03" id="id3">CUHK03 (<code class="docutils literal notranslate"><span class="pre">cuhk03</span></code>)</a></p></li> <li><a class="reference internal" href="#dukemtmc-reid-dagger-dukemtmcreid" id="id4">DukeMTMC-reID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">dukemtmcreid</span></code>)</a></li>
<li><p><a class="reference internal" href="#dukemtmc-reid-dagger-dukemtmcreid" id="id4">DukeMTMC-reID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">dukemtmcreid</span></code>)</a></p></li> <li><a class="reference internal" href="#msmt17-msmt17" id="id5">MSMT17 (<code class="docutils literal notranslate"><span class="pre">msmt17</span></code>)</a></li>
<li><p><a class="reference internal" href="#msmt17-msmt17" id="id5">MSMT17 (<code class="docutils literal notranslate"><span class="pre">msmt17</span></code>)</a></p></li> <li><a class="reference internal" href="#viper-dagger-viper" id="id6">VIPeR <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">viper</span></code>)</a></li>
<li><p><a class="reference internal" href="#viper-dagger-viper" id="id6">VIPeR <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">viper</span></code>)</a></p></li> <li><a class="reference internal" href="#grid-dagger-grid" id="id7">GRID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">grid</span></code>)</a></li>
<li><p><a class="reference internal" href="#grid-dagger-grid" id="id7">GRID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">grid</span></code>)</a></p></li> <li><a class="reference internal" href="#cuhk01-cuhk01" id="id8">CUHK01 (<code class="docutils literal notranslate"><span class="pre">cuhk01</span></code>)</a></li>
<li><p><a class="reference internal" href="#cuhk01-cuhk01" id="id8">CUHK01 (<code class="docutils literal notranslate"><span class="pre">cuhk01</span></code>)</a></p></li> <li><a class="reference internal" href="#sensereid-sensereid" id="id9">SenseReID (<code class="docutils literal notranslate"><span class="pre">sensereid</span></code>)</a></li>
<li><p><a class="reference internal" href="#sensereid-sensereid" id="id9">SenseReID (<code class="docutils literal notranslate"><span class="pre">sensereid</span></code>)</a></p></li> <li><a class="reference internal" href="#qmul-ilids-dagger-ilids" id="id10">QMUL-iLIDS <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">ilids</span></code>)</a></li>
<li><p><a class="reference internal" href="#qmul-ilids-dagger-ilids" id="id10">QMUL-iLIDS <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">ilids</span></code>)</a></p></li> <li><a class="reference internal" href="#prid-prid" id="id11">PRID (<code class="docutils literal notranslate"><span class="pre">prid</span></code>)</a></li>
<li><p><a class="reference internal" href="#prid-prid" id="id11">PRID (<code class="docutils literal notranslate"><span class="pre">prid</span></code>)</a></p></li> <li><a class="reference internal" href="#cuhk02-cuhk02" id="id12">CUHK02 (<code class="docutils literal notranslate"><span class="pre">cuhk02</span></code>)</a></li>
<li><p><a class="reference internal" href="#cuhk02-cuhk02" id="id12">CUHK02 (<code class="docutils literal notranslate"><span class="pre">cuhk02</span></code>)</a></p></li>
</ul> </ul>
</li> </li>
<li><p><a class="reference internal" href="#video-datasets" id="id13">Video Datasets</a></p> <li><a class="reference internal" href="#video-datasets" id="id13">Video Datasets</a><ul>
<ul> <li><a class="reference internal" href="#mars-mars" id="id14">MARS (<code class="docutils literal notranslate"><span class="pre">mars</span></code>)</a></li>
<li><p><a class="reference internal" href="#mars-mars" id="id14">MARS (<code class="docutils literal notranslate"><span class="pre">mars</span></code>)</a></p></li> <li><a class="reference internal" href="#ilids-vid-dagger-ilidsvid" id="id15">iLIDS-VID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">ilidsvid</span></code>)</a></li>
<li><p><a class="reference internal" href="#ilids-vid-dagger-ilidsvid" id="id15">iLIDS-VID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">ilidsvid</span></code>)</a></p></li> <li><a class="reference internal" href="#prid2011-prid2011" id="id16">PRID2011 (<code class="docutils literal notranslate"><span class="pre">prid2011</span></code>)</a></li>
<li><p><a class="reference internal" href="#prid2011-prid2011" id="id16">PRID2011 (<code class="docutils literal notranslate"><span class="pre">prid2011</span></code>)</a></p></li> <li><a class="reference internal" href="#dukemtmc-videoreid-dagger-dukemtmcvidreid" id="id17">DukeMTMC-VideoReID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">dukemtmcvidreid</span></code>)</a></li>
<li><p><a class="reference internal" href="#dukemtmc-videoreid-dagger-dukemtmcvidreid" id="id17">DukeMTMC-VideoReID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">dukemtmcvidreid</span></code>)</a></p></li>
</ul> </ul>
</li> </li>
</ul> </ul>
@ -240,9 +238,9 @@
<div class="section" id="market1501-dagger-market1501"> <div class="section" id="market1501-dagger-market1501">
<h3><a class="toc-backref" href="#id2">Market1501 <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">market1501</span></code>)</a><a class="headerlink" href="#market1501-dagger-market1501" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id2">Market1501 <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">market1501</span></code>)</a><a class="headerlink" href="#market1501-dagger-market1501" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create a directory named “market1501” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create a directory named “market1501” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download the dataset to “market1501” from <a class="reference external" href="http://www.liangzheng.org/Project/project_reid.html">http://www.liangzheng.org/Project/project_reid.html</a> and extract the files.</p></li> <li>Download the dataset to “market1501” from <a class="reference external" href="http://www.liangzheng.org/Project/project_reid.html">http://www.liangzheng.org/Project/project_reid.html</a> and extract the files.</li>
<li><p>The data structure should look like</p></li> <li>The data structure should look like</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>market1501/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>market1501/
Market-1501-v15.09.15/ Market-1501-v15.09.15/
@ -252,16 +250,16 @@
</pre></div> </pre></div>
</div> </div>
<ul class="simple"> <ul class="simple">
<li><p>To use the extra 500K distractors (i.e. Market1501 + 500K), go to the <strong>Market-1501+500k Dataset</strong> section at <a class="reference external" href="http://www.liangzheng.org/Project/project_reid.html">http://www.liangzheng.org/Project/project_reid.html</a>, download the zip file “distractors_500k.zip” and extract it under “market1501/Market-1501-v15.09.15”. The argument to use these 500K distrctors is <code class="docutils literal notranslate"><span class="pre">market1501_500k</span></code> in <code class="docutils literal notranslate"><span class="pre">ImageDataManager</span></code>.</p></li> <li>To use the extra 500K distractors (i.e. Market1501 + 500K), go to the <strong>Market-1501+500k Dataset</strong> section at <a class="reference external" href="http://www.liangzheng.org/Project/project_reid.html">http://www.liangzheng.org/Project/project_reid.html</a>, download the zip file “distractors_500k.zip” and extract it under “market1501/Market-1501-v15.09.15”. The argument to use these 500K distrctors is <code class="docutils literal notranslate"><span class="pre">market1501_500k</span></code> in <code class="docutils literal notranslate"><span class="pre">ImageDataManager</span></code>.</li>
</ul> </ul>
</div> </div>
<div class="section" id="cuhk03-cuhk03"> <div class="section" id="cuhk03-cuhk03">
<h3><a class="toc-backref" href="#id3">CUHK03 (<code class="docutils literal notranslate"><span class="pre">cuhk03</span></code>)</a><a class="headerlink" href="#cuhk03-cuhk03" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id3">CUHK03 (<code class="docutils literal notranslate"><span class="pre">cuhk03</span></code>)</a><a class="headerlink" href="#cuhk03-cuhk03" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create a folder named “cuhk03” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create a folder named “cuhk03” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download the dataset to “cuhk03/” from <a class="reference external" href="http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html">http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html</a> and extract “cuhk03_release.zip”, resulting in “cuhk03/cuhk03_release/”.</p></li> <li>Download the dataset to “cuhk03/” from <a class="reference external" href="http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html">http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html</a> and extract “cuhk03_release.zip”, resulting in “cuhk03/cuhk03_release/”.</li>
<li><p>Download the new split (767/700) from <a class="reference external" href="https://github.com/zhunzhong07/person-re-ranking/tree/master/evaluation/data/CUHK03">person-re-ranking</a>. What you need are “cuhk03_new_protocol_config_detected.mat” and “cuhk03_new_protocol_config_labeled.mat”. Put these two mat files under “cuhk03/”.</p></li> <li>Download the new split (767/700) from <a class="reference external" href="https://github.com/zhunzhong07/person-re-ranking/tree/master/evaluation/data/CUHK03">person-re-ranking</a>. What you need are “cuhk03_new_protocol_config_detected.mat” and “cuhk03_new_protocol_config_labeled.mat”. Put these two mat files under “cuhk03/”.</li>
<li><p>The data structure should look like</p></li> <li>The data structure should look like</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>cuhk03/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>cuhk03/
cuhk03_release/ cuhk03_release/
@ -270,19 +268,19 @@
</pre></div> </pre></div>
</div> </div>
<ul class="simple"> <ul class="simple">
<li><p>In the default mode, we load data using the new split (767/700). If you wanna use the original (20) splits (1367/100), please set <code class="docutils literal notranslate"><span class="pre">cuhk03_classic_split</span></code> to True in <code class="docutils literal notranslate"><span class="pre">ImageDataManager</span></code>. As the CMC is computed differently from Market1501 for the 1367/100 split (see <a class="reference external" href="http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html">here</a>), you need to enable <code class="docutils literal notranslate"><span class="pre">use_metric_cuhk03</span></code> in <code class="docutils literal notranslate"><span class="pre">ImageDataManager</span></code> to activate the <em>single-gallery-shot</em> metric for fair comparison with some methods that adopt the old splits (<em>do not need to report mAP</em>). In addition, we support both <em>labeled</em> and <em>detected</em> modes. The default mode loads <em>detected</em> images. Enable <code class="docutils literal notranslate"><span class="pre">cuhk03_labeled</span></code> in <code class="docutils literal notranslate"><span class="pre">ImageDataManager</span></code> if you wanna train and test on <em>labeled</em> images.</p></li> <li>In the default mode, we load data using the new split (767/700). If you wanna use the original (20) splits (1367/100), please set <code class="docutils literal notranslate"><span class="pre">cuhk03_classic_split</span></code> to True in <code class="docutils literal notranslate"><span class="pre">ImageDataManager</span></code>. As the CMC is computed differently from Market1501 for the 1367/100 split (see <a class="reference external" href="http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html">here</a>), you need to enable <code class="docutils literal notranslate"><span class="pre">use_metric_cuhk03</span></code> in <code class="docutils literal notranslate"><span class="pre">ImageDataManager</span></code> to activate the <em>single-gallery-shot</em> metric for fair comparison with some methods that adopt the old splits (<em>do not need to report mAP</em>). In addition, we support both <em>labeled</em> and <em>detected</em> modes. The default mode loads <em>detected</em> images. Enable <code class="docutils literal notranslate"><span class="pre">cuhk03_labeled</span></code> in <code class="docutils literal notranslate"><span class="pre">ImageDataManager</span></code> if you wanna train and test on <em>labeled</em> images.</li>
</ul> </ul>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>The code will extract images in “cuhk-03.mat” and save them under “cuhk03/images_detected” and “cuhk03/images_labeled”. Also, four json files will be automatically generated, i.e. “splits_classic_detected.json”, “splits_classic_labeled.json”, “splits_new_detected.json” and “splits_new_labeled.json”. If the parent path of <code class="docutils literal notranslate"><span class="pre">$REID</span></code> is changed, these json files should be manually deleted. The code can automatically generate new json files to match the new path.</p> <p class="last">The code will extract images in “cuhk-03.mat” and save them under “cuhk03/images_detected” and “cuhk03/images_labeled”. Also, four json files will be automatically generated, i.e. “splits_classic_detected.json”, “splits_classic_labeled.json”, “splits_new_detected.json” and “splits_new_labeled.json”. If the parent path of <code class="docutils literal notranslate"><span class="pre">$REID</span></code> is changed, these json files should be manually deleted. The code can automatically generate new json files to match the new path.</p>
</div> </div>
</div> </div>
<div class="section" id="dukemtmc-reid-dagger-dukemtmcreid"> <div class="section" id="dukemtmc-reid-dagger-dukemtmcreid">
<h3><a class="toc-backref" href="#id4">DukeMTMC-reID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">dukemtmcreid</span></code>)</a><a class="headerlink" href="#dukemtmc-reid-dagger-dukemtmcreid" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id4">DukeMTMC-reID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">dukemtmcreid</span></code>)</a><a class="headerlink" href="#dukemtmc-reid-dagger-dukemtmcreid" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create a directory called “dukemtmc-reid” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create a directory called “dukemtmc-reid” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download “DukeMTMC-reID” from <a class="reference external" href="http://vision.cs.duke.edu/DukeMTMC/">http://vision.cs.duke.edu/DukeMTMC/</a> and extract it under “dukemtmc-reid”.</p></li> <li>Download “DukeMTMC-reID” from <a class="reference external" href="http://vision.cs.duke.edu/DukeMTMC/">http://vision.cs.duke.edu/DukeMTMC/</a> and extract it under “dukemtmc-reid”.</li>
<li><p>The data structure should look like</p></li> <li>The data structure should look like</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>dukemtmc-reid/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>dukemtmc-reid/
DukeMTMC-reID/ DukeMTMC-reID/
@ -296,9 +294,9 @@
<div class="section" id="msmt17-msmt17"> <div class="section" id="msmt17-msmt17">
<h3><a class="toc-backref" href="#id5">MSMT17 (<code class="docutils literal notranslate"><span class="pre">msmt17</span></code>)</a><a class="headerlink" href="#msmt17-msmt17" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id5">MSMT17 (<code class="docutils literal notranslate"><span class="pre">msmt17</span></code>)</a><a class="headerlink" href="#msmt17-msmt17" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create a directory called “msmt17” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create a directory called “msmt17” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download the dataset from <a class="reference external" href="http://www.pkuvmc.com/publications/msmt17.html">http://www.pkuvmc.com/publications/msmt17.html</a> to “msmt17” and extract the files.</p></li> <li>Download the dataset from <a class="reference external" href="http://www.pkuvmc.com/publications/msmt17.html">http://www.pkuvmc.com/publications/msmt17.html</a> to “msmt17” and extract the files.</li>
<li><p>The data structure should look like</p></li> <li>The data structure should look like</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>msmt17/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>msmt17/
MSMT17_V1/ # or MSMT17_V2 MSMT17_V1/ # or MSMT17_V2
@ -314,8 +312,8 @@
<div class="section" id="viper-dagger-viper"> <div class="section" id="viper-dagger-viper">
<h3><a class="toc-backref" href="#id6">VIPeR <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">viper</span></code>)</a><a class="headerlink" href="#viper-dagger-viper" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id6">VIPeR <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">viper</span></code>)</a><a class="headerlink" href="#viper-dagger-viper" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>The download link is <a class="reference external" href="http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip">http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip</a>.</p></li> <li>The download link is <a class="reference external" href="http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip">http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip</a>.</li>
<li><p>Organize the dataset in a folder named “viper” as follows</p></li> <li>Organize the dataset in a folder named “viper” as follows</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>viper/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>viper/
VIPeR/ VIPeR/
@ -327,8 +325,8 @@
<div class="section" id="grid-dagger-grid"> <div class="section" id="grid-dagger-grid">
<h3><a class="toc-backref" href="#id7">GRID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">grid</span></code>)</a><a class="headerlink" href="#grid-dagger-grid" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id7">GRID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">grid</span></code>)</a><a class="headerlink" href="#grid-dagger-grid" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>The download link is <a class="reference external" href="http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip">http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip</a>.</p></li> <li>The download link is <a class="reference external" href="http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip">http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip</a>.</li>
<li><p>Organize the dataset in a folder named “grid” as follows</p></li> <li>Organize the dataset in a folder named “grid” as follows</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>grid/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>grid/
underground_reid/ underground_reid/
@ -341,10 +339,10 @@
<div class="section" id="cuhk01-cuhk01"> <div class="section" id="cuhk01-cuhk01">
<h3><a class="toc-backref" href="#id8">CUHK01 (<code class="docutils literal notranslate"><span class="pre">cuhk01</span></code>)</a><a class="headerlink" href="#cuhk01-cuhk01" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id8">CUHK01 (<code class="docutils literal notranslate"><span class="pre">cuhk01</span></code>)</a><a class="headerlink" href="#cuhk01-cuhk01" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create a folder named “cuhk01” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create a folder named “cuhk01” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download “CUHK01.zip” from <a class="reference external" href="http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html">http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html</a> and place it under “cuhk01/”.</p></li> <li>Download “CUHK01.zip” from <a class="reference external" href="http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html">http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html</a> and place it under “cuhk01/”.</li>
<li><p>The code can automatically extract the files, or you can do it yourself.</p></li> <li>The code can automatically extract the files, or you can do it yourself.</li>
<li><p>The data structure should look like</p></li> <li>The data structure should look like</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>cuhk01/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>cuhk01/
campus/ campus/
@ -354,9 +352,9 @@
<div class="section" id="sensereid-sensereid"> <div class="section" id="sensereid-sensereid">
<h3><a class="toc-backref" href="#id9">SenseReID (<code class="docutils literal notranslate"><span class="pre">sensereid</span></code>)</a><a class="headerlink" href="#sensereid-sensereid" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id9">SenseReID (<code class="docutils literal notranslate"><span class="pre">sensereid</span></code>)</a><a class="headerlink" href="#sensereid-sensereid" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create “sensereid” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create “sensereid” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download the dataset from this <a class="reference external" href="https://drive.google.com/file/d/0B56OfSrVI8hubVJLTzkwV2VaOWM/view">link</a> and extract it to “sensereid”.</p></li> <li>Download the dataset from this <a class="reference external" href="https://drive.google.com/file/d/0B56OfSrVI8hubVJLTzkwV2VaOWM/view">link</a> and extract it to “sensereid”.</li>
<li><p>Organize the data to be like</p></li> <li>Organize the data to be like</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>sensereid/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>sensereid/
SenseReID/ SenseReID/
@ -368,8 +366,8 @@
<div class="section" id="qmul-ilids-dagger-ilids"> <div class="section" id="qmul-ilids-dagger-ilids">
<h3><a class="toc-backref" href="#id10">QMUL-iLIDS <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">ilids</span></code>)</a><a class="headerlink" href="#qmul-ilids-dagger-ilids" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id10">QMUL-iLIDS <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">ilids</span></code>)</a><a class="headerlink" href="#qmul-ilids-dagger-ilids" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create a folder named “ilids” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create a folder named “ilids” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download the dataset from <a class="reference external" href="http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz">http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz</a> and organize it to look like</p></li> <li>Download the dataset from <a class="reference external" href="http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz">http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz</a> and organize it to look like</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>ilids/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>ilids/
i-LIDS_Pedestrian/ i-LIDS_Pedestrian/
@ -380,9 +378,9 @@
<div class="section" id="prid-prid"> <div class="section" id="prid-prid">
<h3><a class="toc-backref" href="#id11">PRID (<code class="docutils literal notranslate"><span class="pre">prid</span></code>)</a><a class="headerlink" href="#prid-prid" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id11">PRID (<code class="docutils literal notranslate"><span class="pre">prid</span></code>)</a><a class="headerlink" href="#prid-prid" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create a directory named “prid2011” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create a directory named “prid2011” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download the dataset from <a class="reference external" href="https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/">https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/</a> and extract it under “prid2011”.</p></li> <li>Download the dataset from <a class="reference external" href="https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/">https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/</a> and extract it under “prid2011”.</li>
<li><p>The data structure should end up with</p></li> <li>The data structure should end up with</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>prid2011/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>prid2011/
prid_2011/ prid_2011/
@ -394,9 +392,9 @@
<div class="section" id="cuhk02-cuhk02"> <div class="section" id="cuhk02-cuhk02">
<h3><a class="toc-backref" href="#id12">CUHK02 (<code class="docutils literal notranslate"><span class="pre">cuhk02</span></code>)</a><a class="headerlink" href="#cuhk02-cuhk02" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id12">CUHK02 (<code class="docutils literal notranslate"><span class="pre">cuhk02</span></code>)</a><a class="headerlink" href="#cuhk02-cuhk02" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create a folder named “cuhk02” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create a folder named “cuhk02” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download the data from <a class="reference external" href="http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html">http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html</a> and put it under “cuhk02/”.</p></li> <li>Download the data from <a class="reference external" href="http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html">http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html</a> and put it under “cuhk02/”.</li>
<li><p>Extract the file so the data structure looks like</p></li> <li>Extract the file so the data structure looks like</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>cuhk02/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>cuhk02/
Dataset/ Dataset/
@ -414,11 +412,11 @@
<div class="section" id="mars-mars"> <div class="section" id="mars-mars">
<h3><a class="toc-backref" href="#id14">MARS (<code class="docutils literal notranslate"><span class="pre">mars</span></code>)</a><a class="headerlink" href="#mars-mars" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id14">MARS (<code class="docutils literal notranslate"><span class="pre">mars</span></code>)</a><a class="headerlink" href="#mars-mars" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create “mars/” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create “mars/” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download the dataset from <a class="reference external" href="http://www.liangzheng.com.cn/Project/project_mars.html">http://www.liangzheng.com.cn/Project/project_mars.html</a> and place it in “mars/”.</p></li> <li>Download the dataset from <a class="reference external" href="http://www.liangzheng.com.cn/Project/project_mars.html">http://www.liangzheng.com.cn/Project/project_mars.html</a> and place it in “mars/”.</li>
<li><p>Extract “bbox_train.zip” and “bbox_test.zip”.</p></li> <li>Extract “bbox_train.zip” and “bbox_test.zip”.</li>
<li><p>Download the split metadata from <a class="reference external" href="https://github.com/liangzheng06/MARS-evaluation/tree/master/info">https://github.com/liangzheng06/MARS-evaluation/tree/master/info</a> and put “info/” in “mars/”.</p></li> <li>Download the split metadata from <a class="reference external" href="https://github.com/liangzheng06/MARS-evaluation/tree/master/info">https://github.com/liangzheng06/MARS-evaluation/tree/master/info</a> and put “info/” in “mars/”.</li>
<li><p>The data structure should end up with</p></li> <li>The data structure should end up with</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>mars/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>mars/
bbox_test/ bbox_test/
@ -430,9 +428,9 @@
<div class="section" id="ilids-vid-dagger-ilidsvid"> <div class="section" id="ilids-vid-dagger-ilidsvid">
<h3><a class="toc-backref" href="#id15">iLIDS-VID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">ilidsvid</span></code>)</a><a class="headerlink" href="#ilids-vid-dagger-ilidsvid" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id15">iLIDS-VID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">ilidsvid</span></code>)</a><a class="headerlink" href="#ilids-vid-dagger-ilidsvid" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create “ilids-vid” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create “ilids-vid” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download the dataset from <a class="reference external" href="http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html">http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html</a> to “ilids-vid”.</p></li> <li>Download the dataset from <a class="reference external" href="http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html">http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html</a> to “ilids-vid”.</li>
<li><p>Organize the data structure to match</p></li> <li>Organize the data structure to match</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>ilids-vid/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>ilids-vid/
i-LIDS-VID/ i-LIDS-VID/
@ -443,10 +441,10 @@
<div class="section" id="prid2011-prid2011"> <div class="section" id="prid2011-prid2011">
<h3><a class="toc-backref" href="#id16">PRID2011 (<code class="docutils literal notranslate"><span class="pre">prid2011</span></code>)</a><a class="headerlink" href="#prid2011-prid2011" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id16">PRID2011 (<code class="docutils literal notranslate"><span class="pre">prid2011</span></code>)</a><a class="headerlink" href="#prid2011-prid2011" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create a directory named “prid2011” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create a directory named “prid2011” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download the dataset from <a class="reference external" href="https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/">https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/</a> and extract it under “prid2011”.</p></li> <li>Download the dataset from <a class="reference external" href="https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/">https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/</a> and extract it under “prid2011”.</li>
<li><p>Download the split created by <em>iLIDS-VID</em> from <a class="reference external" href="https://drive.google.com/open?id=1qw7SI7YdIgfHetIQO7LLW4SHpL_qkieT">this google drive</a> and put it under “prid2011/”. Following the standard protocol, only 178 persons whose sequences are more than a threshold are used.</p></li> <li>Download the split created by <em>iLIDS-VID</em> from <a class="reference external" href="https://drive.google.com/open?id=1qw7SI7YdIgfHetIQO7LLW4SHpL_qkieT">this google drive</a> and put it under “prid2011/”. Following the standard protocol, only 178 persons whose sequences are more than a threshold are used.</li>
<li><p>The data structure should end up with</p></li> <li>The data structure should end up with</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>prid2011/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>prid2011/
splits_prid2011.json splits_prid2011.json
@ -459,9 +457,9 @@
<div class="section" id="dukemtmc-videoreid-dagger-dukemtmcvidreid"> <div class="section" id="dukemtmc-videoreid-dagger-dukemtmcvidreid">
<h3><a class="toc-backref" href="#id17">DukeMTMC-VideoReID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">dukemtmcvidreid</span></code>)</a><a class="headerlink" href="#dukemtmc-videoreid-dagger-dukemtmcvidreid" title="Permalink to this headline"></a></h3> <h3><a class="toc-backref" href="#id17">DukeMTMC-VideoReID <span class="math notranslate nohighlight">\(^\dagger\)</span> (<code class="docutils literal notranslate"><span class="pre">dukemtmcvidreid</span></code>)</a><a class="headerlink" href="#dukemtmc-videoreid-dagger-dukemtmcvidreid" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Create “dukemtmc-vidreid” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</p></li> <li>Create “dukemtmc-vidreid” under <code class="docutils literal notranslate"><span class="pre">$REID</span></code>.</li>
<li><p>Download “DukeMTMC-VideoReID” from <a class="reference external" href="http://vision.cs.duke.edu/DukeMTMC/">http://vision.cs.duke.edu/DukeMTMC/</a> and unzip the file to “dukemtmc-vidreid/”.</p></li> <li>Download “DukeMTMC-VideoReID” from <a class="reference external" href="http://vision.cs.duke.edu/DukeMTMC/">http://vision.cs.duke.edu/DukeMTMC/</a> and unzip the file to “dukemtmc-vidreid/”.</li>
<li><p>The data structure should look like</p></li> <li>The data structure should look like</li>
</ul> </ul>
<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>dukemtmc-vidreid/ <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>dukemtmc-vidreid/
DukeMTMC-VideoReID/ DukeMTMC-VideoReID/

20
evaluation.html
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@ -177,23 +177,23 @@
<div class="section" id="image-reid"> <div class="section" id="image-reid">
<h2>Image ReID<a class="headerlink" href="#image-reid" title="Permalink to this headline"></a></h2> <h2>Image ReID<a class="headerlink" href="#image-reid" title="Permalink to this headline"></a></h2>
<ul class="simple"> <ul class="simple">
<li><p><strong>Market1501</strong>, <strong>DukeMTMC-reID</strong>, <strong>CUHK03 (767/700 split)</strong> and <strong>MSMT17</strong> have fixed split so keeping <code class="docutils literal notranslate"><span class="pre">split_id=0</span></code> is fine.</p></li> <li><strong>Market1501</strong>, <strong>DukeMTMC-reID</strong>, <strong>CUHK03 (767/700 split)</strong> and <strong>MSMT17</strong> have fixed split so keeping <code class="docutils literal notranslate"><span class="pre">split_id=0</span></code> is fine.</li>
<li><p><strong>CUHK03 (classic split)</strong> has 20 fixed splits, so do <code class="docutils literal notranslate"><span class="pre">split_id=0~19</span></code>.</p></li> <li><strong>CUHK03 (classic split)</strong> has 20 fixed splits, so do <code class="docutils literal notranslate"><span class="pre">split_id=0~19</span></code>.</li>
<li><p><strong>VIPeR</strong> contains 632 identities each with 2 images under two camera views. Evaluation should be done for 10 random splits. Each split randomly divides 632 identities to 316 train ids (632 images) and the other 316 test ids (632 images). Note that, in each random split, there are two sub-splits, one using camera-A as query and camera-B as gallery while the other one using camera-B as query and camera-A as gallery. Thus, there are totally 20 splits generated with <code class="docutils literal notranslate"><span class="pre">split_id</span></code> starting from 0 to 19. Models can be trained on <code class="docutils literal notranslate"><span class="pre">split_id=[0,</span> <span class="pre">2,</span> <span class="pre">4,</span> <span class="pre">6,</span> <span class="pre">8,</span> <span class="pre">10,</span> <span class="pre">12,</span> <span class="pre">14,</span> <span class="pre">16,</span> <span class="pre">18]</span></code> (because <code class="docutils literal notranslate"><span class="pre">split_id=0</span></code> and <code class="docutils literal notranslate"><span class="pre">split_id=1</span></code> share the same train set, and so on and so forth.). At test time, models trained on <code class="docutils literal notranslate"><span class="pre">split_id=0</span></code> can be directly evaluated on <code class="docutils literal notranslate"><span class="pre">split_id=1</span></code>, models trained on <code class="docutils literal notranslate"><span class="pre">split_id=2</span></code> can be directly evaluated on <code class="docutils literal notranslate"><span class="pre">split_id=3</span></code>, and so on and so forth.</p></li> <li><strong>VIPeR</strong> contains 632 identities each with 2 images under two camera views. Evaluation should be done for 10 random splits. Each split randomly divides 632 identities to 316 train ids (632 images) and the other 316 test ids (632 images). Note that, in each random split, there are two sub-splits, one using camera-A as query and camera-B as gallery while the other one using camera-B as query and camera-A as gallery. Thus, there are totally 20 splits generated with <code class="docutils literal notranslate"><span class="pre">split_id</span></code> starting from 0 to 19. Models can be trained on <code class="docutils literal notranslate"><span class="pre">split_id=[0,</span> <span class="pre">2,</span> <span class="pre">4,</span> <span class="pre">6,</span> <span class="pre">8,</span> <span class="pre">10,</span> <span class="pre">12,</span> <span class="pre">14,</span> <span class="pre">16,</span> <span class="pre">18]</span></code> (because <code class="docutils literal notranslate"><span class="pre">split_id=0</span></code> and <code class="docutils literal notranslate"><span class="pre">split_id=1</span></code> share the same train set, and so on and so forth.). At test time, models trained on <code class="docutils literal notranslate"><span class="pre">split_id=0</span></code> can be directly evaluated on <code class="docutils literal notranslate"><span class="pre">split_id=1</span></code>, models trained on <code class="docutils literal notranslate"><span class="pre">split_id=2</span></code> can be directly evaluated on <code class="docutils literal notranslate"><span class="pre">split_id=3</span></code>, and so on and so forth.</li>
<li><p><strong>CUHK01</strong> is similar to VIPeR in the split generation.</p></li> <li><strong>CUHK01</strong> is similar to VIPeR in the split generation.</li>
<li><p><strong>GRID</strong> , <strong>iLIDS</strong> and <strong>PRID</strong> have 10 random splits, so evaluation should be done by varying <code class="docutils literal notranslate"><span class="pre">split_id</span></code> from 0 to 9.</p></li> <li><strong>GRID</strong> , <strong>iLIDS</strong> and <strong>PRID</strong> have 10 random splits, so evaluation should be done by varying <code class="docutils literal notranslate"><span class="pre">split_id</span></code> from 0 to 9.</li>
<li><p><strong>SenseReID</strong> has no training images and is used for evaluation only.</p></li> <li><strong>SenseReID</strong> has no training images and is used for evaluation only.</li>
</ul> </ul>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>The <code class="docutils literal notranslate"><span class="pre">split_id</span></code> argument is defined in <code class="docutils literal notranslate"><span class="pre">ImageDataManager</span></code> and <code class="docutils literal notranslate"><span class="pre">VideoDataManager</span></code>. Please refer to <a class="reference internal" href="pkg/data.html#torchreid-data"><span class="std std-ref">torchreid.data</span></a>.</p> <p class="last">The <code class="docutils literal notranslate"><span class="pre">split_id</span></code> argument is defined in <code class="docutils literal notranslate"><span class="pre">ImageDataManager</span></code> and <code class="docutils literal notranslate"><span class="pre">VideoDataManager</span></code>. Please refer to <a class="reference internal" href="pkg/data.html#torchreid-data"><span class="std std-ref">torchreid.data</span></a>.</p>
</div> </div>
</div> </div>
<div class="section" id="video-reid"> <div class="section" id="video-reid">
<h2>Video ReID<a class="headerlink" href="#video-reid" title="Permalink to this headline"></a></h2> <h2>Video ReID<a class="headerlink" href="#video-reid" title="Permalink to this headline"></a></h2>
<ul class="simple"> <ul class="simple">
<li><p><strong>MARS</strong> and <strong>DukeMTMC-VideoReID</strong> have fixed single split so using <code class="docutils literal notranslate"><span class="pre">split_id=0</span></code> is ok.</p></li> <li><strong>MARS</strong> and <strong>DukeMTMC-VideoReID</strong> have fixed single split so using <code class="docutils literal notranslate"><span class="pre">split_id=0</span></code> is ok.</li>
<li><p><strong>iLIDS-VID</strong> and <strong>PRID2011</strong> have 10 predefined splits so evaluation should be done by varying <code class="docutils literal notranslate"><span class="pre">split_id</span></code> from 0 to 9.</p></li> <li><strong>iLIDS-VID</strong> and <strong>PRID2011</strong> have 10 predefined splits so evaluation should be done by varying <code class="docutils literal notranslate"><span class="pre">split_id</span></code> from 0 to 9.</li>
</ul> </ul>
</div> </div>
</div> </div>

4
genindex.html
View File

@ -398,9 +398,9 @@
</li> </li>
</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="pkg/data.html#torchreid.data.datamanager.DataManager.num_train_cams">num_train_cams() (torchreid.data.datamanager.DataManager property)</a> <li><a href="pkg/data.html#torchreid.data.datamanager.DataManager.num_train_cams">num_train_cams (torchreid.data.datamanager.DataManager attribute)</a>
</li> </li>
<li><a href="pkg/data.html#torchreid.data.datamanager.DataManager.num_train_pids">num_train_pids() (torchreid.data.datamanager.DataManager property)</a> <li><a href="pkg/data.html#torchreid.data.datamanager.DataManager.num_train_pids">num_train_pids (torchreid.data.datamanager.DataManager attribute)</a>
</li> </li>
</ul></td> </ul></td>
</tr></table> </tr></table>

116
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@ -173,18 +173,18 @@
<p>Torchreid is a library for deep-learning person re-identification in <a class="reference external" href="https://pytorch.org/">PyTorch</a>.</p> <p>Torchreid is a library for deep-learning person re-identification in <a class="reference external" href="https://pytorch.org/">PyTorch</a>.</p>
<p>It features:</p> <p>It features:</p>
<ul class="simple"> <ul class="simple">
<li><p>multi-GPU training</p></li> <li>multi-GPU training</li>
<li><p>support both image- and video-reid</p></li> <li>support both image- and video-reid</li>
<li><p>end-to-end training and evaluation</p></li> <li>end-to-end training and evaluation</li>
<li><p>incredibly easy preparation of reid datasets</p></li> <li>incredibly easy preparation of reid datasets</li>
<li><p>multi-dataset training</p></li> <li>multi-dataset training</li>
<li><p>cross-dataset evaluation</p></li> <li>cross-dataset evaluation</li>
<li><p>standard protocol used by most research papers</p></li> <li>standard protocol used by most research papers</li>
<li><p>highly extensible (easy to add models, datasets, training methods, etc.)</p></li> <li>highly extensible (easy to add models, datasets, training methods, etc.)</li>
<li><p>implementations of state-of-the-art deep reid models</p></li> <li>implementations of state-of-the-art deep reid models</li>
<li><p>access to pretrained reid models</p></li> <li>access to pretrained reid models</li>
<li><p>advanced training techniques</p></li> <li>advanced training techniques</li>
<li><p>visualization tools (tensorboard, ranks, etc.)</p></li> <li>visualization tools (tensorboard, ranks, etc.)</li>
</ul> </ul>
<p>Code: <a class="reference external" href="https://github.com/KaiyangZhou/deep-person-reid">https://github.com/KaiyangZhou/deep-person-reid</a>.</p> <p>Code: <a class="reference external" href="https://github.com/KaiyangZhou/deep-person-reid">https://github.com/KaiyangZhou/deep-person-reid</a>.</p>
<p>Documentation: <a class="reference external" href="https://kaiyangzhou.github.io/deep-person-reid/">https://kaiyangzhou.github.io/deep-person-reid/</a>.</p> <p>Documentation: <a class="reference external" href="https://kaiyangzhou.github.io/deep-person-reid/">https://kaiyangzhou.github.io/deep-person-reid/</a>.</p>
@ -218,13 +218,13 @@ python setup.py develop
<div class="section" id="get-started-30-seconds-to-torchreid"> <div class="section" id="get-started-30-seconds-to-torchreid">
<h2>Get started: 30 seconds to Torchreid<a class="headerlink" href="#get-started-30-seconds-to-torchreid" title="Permalink to this headline"></a></h2> <h2>Get started: 30 seconds to Torchreid<a class="headerlink" href="#get-started-30-seconds-to-torchreid" title="Permalink to this headline"></a></h2>
<ol class="arabic simple"> <ol class="arabic simple">
<li><p>Import <code class="docutils literal notranslate"><span class="pre">torchreid</span></code></p></li> <li>Import <code class="docutils literal notranslate"><span class="pre">torchreid</span></code></li>
</ol> </ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchreid</span> <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchreid</span>
</pre></div> </pre></div>
</div> </div>
<ol class="arabic simple" start="2"> <ol class="arabic simple" start="2">
<li><p>Load data manager</p></li> <li>Load data manager</li>
</ol> </ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ImageDataManager</span><span class="p">(</span> <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ImageDataManager</span><span class="p">(</span>
<span class="n">root</span><span class="o">=</span><span class="s1">&#39;reid-data&#39;</span><span class="p">,</span> <span class="n">root</span><span class="o">=</span><span class="s1">&#39;reid-data&#39;</span><span class="p">,</span>
@ -262,7 +262,7 @@ python setup.py develop
</pre></div> </pre></div>
</div> </div>
<ol class="arabic simple" start="4"> <ol class="arabic simple" start="4">
<li><p>Build engine</p></li> <li>Build engine</li>
</ol> </ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">engine</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">ImageSoftmaxEngine</span><span class="p">(</span> <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">engine</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">ImageSoftmaxEngine</span><span class="p">(</span>
<span class="n">datamanager</span><span class="p">,</span> <span class="n">datamanager</span><span class="p">,</span>
@ -274,7 +274,7 @@ python setup.py develop
</pre></div> </pre></div>
</div> </div>
<ol class="arabic simple" start="5"> <ol class="arabic simple" start="5">
<li><p>Run training and test</p></li> <li>Run training and test</li>
</ol> </ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">engine</span><span class="o">.</span><span class="n">run</span><span class="p">(</span> <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">engine</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
<span class="n">save_dir</span><span class="o">=</span><span class="s1">&#39;log/resnet50&#39;</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="s1">&#39;log/resnet50&#39;</span><span class="p">,</span>
@ -289,7 +289,7 @@ python setup.py develop
<div class="section" id="a-unified-interface"> <div class="section" id="a-unified-interface">
<h2>A unified interface<a class="headerlink" href="#a-unified-interface" title="Permalink to this headline"></a></h2> <h2>A unified interface<a class="headerlink" href="#a-unified-interface" title="Permalink to this headline"></a></h2>
<p>In “deep-person-reid/scripts/”, we provide a unified interface to train and test a model. See “scripts/main.py” and “scripts/default_config.py” for more details. “configs/” contains some predefined configs which you can use as a starting point.</p> <p>In “deep-person-reid/scripts/”, we provide a unified interface to train and test a model. See “scripts/main.py” and “scripts/default_config.py” for more details. “configs/” contains some predefined configs which you can use as a starting point.</p>
<p>Below we provide an example to train and test <a class="reference external" href="https://arxiv.org/abs/1905.00953">OSNet (Zhou et al. ICCV’19)</a>. Assume <code class="code docutils literal notranslate"><span class="pre">PATH_TO_DATA</span></code> is the directory containing reid datasets.</p> <p>Below we provide an example to train and test <a class="reference external" href="https://arxiv.org/abs/1905.00953">OSNet (Zhou et al. ICCV‘19)</a>. Assume <code class="code docutils literal notranslate"><span class="pre">PATH_TO_DATA</span></code> is the directory containing reid datasets.</p>
<div class="section" id="conventional-setting"> <div class="section" id="conventional-setting">
<h3>Conventional setting<a class="headerlink" href="#conventional-setting" title="Permalink to this headline"></a></h3> <h3>Conventional setting<a class="headerlink" href="#conventional-setting" title="Permalink to this headline"></a></h3>
<p>To train OSNet on Market1501, do</p> <p>To train OSNet on Market1501, do</p>
@ -344,25 +344,25 @@ test.evaluate True
<div class="section" id="image-reid-datasets"> <div class="section" id="image-reid-datasets">
<h3>Image-reid datasets<a class="headerlink" href="#image-reid-datasets" title="Permalink to this headline"></a></h3> <h3>Image-reid datasets<a class="headerlink" href="#image-reid-datasets" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p><a class="reference external" href="https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Scalable_Person_Re-Identification_ICCV_2015_paper.pdf">Market1501</a></p></li> <li><a class="reference external" href="https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Scalable_Person_Re-Identification_ICCV_2015_paper.pdf">Market1501</a></li>
<li><p><a class="reference external" href="https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf">CUHK03</a></p></li> <li><a class="reference external" href="https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf">CUHK03</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1701.07717">DukeMTMC-reID</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1701.07717">DukeMTMC-reID</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1711.08565">MSMT17</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1711.08565">MSMT17</a></li>
<li><p><a class="reference external" href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.331.7285&amp;rep=rep1&amp;type=pdf">VIPeR</a></p></li> <li><a class="reference external" href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.331.7285&amp;rep=rep1&amp;type=pdf">VIPeR</a></li>
<li><p><a class="reference external" href="http://www.eecs.qmul.ac.uk/~txiang/publications/LoyXiangGong_cvpr_2009.pdf">GRID</a></p></li> <li><a class="reference external" href="http://www.eecs.qmul.ac.uk/~txiang/publications/LoyXiangGong_cvpr_2009.pdf">GRID</a></li>
<li><p><a class="reference external" href="http://www.ee.cuhk.edu.hk/~xgwang/papers/liZWaccv12.pdf">CUHK01</a></p></li> <li><a class="reference external" href="http://www.ee.cuhk.edu.hk/~xgwang/papers/liZWaccv12.pdf">CUHK01</a></li>
<li><p><a class="reference external" href="http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Spindle_Net_Person_CVPR_2017_paper.pdf">SenseReID</a></p></li> <li><a class="reference external" href="http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Spindle_Net_Person_CVPR_2017_paper.pdf">SenseReID</a></li>
<li><p><a class="reference external" href="http://www.eecs.qmul.ac.uk/~sgg/papers/ZhengGongXiang_BMVC09.pdf">QMUL-iLIDS</a></p></li> <li><a class="reference external" href="http://www.eecs.qmul.ac.uk/~sgg/papers/ZhengGongXiang_BMVC09.pdf">QMUL-iLIDS</a></li>
<li><p><a class="reference external" href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf">PRID</a></p></li> <li><a class="reference external" href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf">PRID</a></li>
</ul> </ul>
</div> </div>
<div class="section" id="video-reid-datasets"> <div class="section" id="video-reid-datasets">
<h3>Video-reid datasets<a class="headerlink" href="#video-reid-datasets" title="Permalink to this headline"></a></h3> <h3>Video-reid datasets<a class="headerlink" href="#video-reid-datasets" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p><a class="reference external" href="http://www.liangzheng.org/1320.pdf">MARS</a></p></li> <li><a class="reference external" href="http://www.liangzheng.org/1320.pdf">MARS</a></li>
<li><p><a class="reference external" href="https://www.eecs.qmul.ac.uk/~sgg/papers/WangEtAl_ECCV14.pdf">iLIDS-VID</a></p></li> <li><a class="reference external" href="https://www.eecs.qmul.ac.uk/~sgg/papers/WangEtAl_ECCV14.pdf">iLIDS-VID</a></li>
<li><p><a class="reference external" href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf">PRID2011</a></p></li> <li><a class="reference external" href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf">PRID2011</a></li>
<li><p><a class="reference external" href="http://openaccess.thecvf.com/content_cvpr_2018/papers/Wu_Exploit_the_Unknown_CVPR_2018_paper.pdf">DukeMTMC-VideoReID</a></p></li> <li><a class="reference external" href="http://openaccess.thecvf.com/content_cvpr_2018/papers/Wu_Exploit_the_Unknown_CVPR_2018_paper.pdf">DukeMTMC-VideoReID</a></li>
</ul> </ul>
</div> </div>
</div> </div>
@ -371,44 +371,50 @@ test.evaluate True
<div class="section" id="imagenet-classification-models"> <div class="section" id="imagenet-classification-models">
<h3>ImageNet classification models<a class="headerlink" href="#imagenet-classification-models" title="Permalink to this headline"></a></h3> <h3>ImageNet classification models<a class="headerlink" href="#imagenet-classification-models" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p><a class="reference external" href="https://arxiv.org/abs/1512.03385">ResNet</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1512.03385">ResNet</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1611.05431">ResNeXt</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1611.05431">ResNeXt</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1709.01507">SENet</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1709.01507">SENet</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1608.06993">DenseNet</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1608.06993">DenseNet</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1602.07261">Inception-ResNet-V2</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1602.07261">Inception-ResNet-V2</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1602.07261">Inception-V4</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1602.07261">Inception-V4</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1610.02357">Xception</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1610.02357">Xception</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1807.09441">IBN-Net</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1807.09441">IBN-Net</a></li>
</ul> </ul>
</div> </div>
<div class="section" id="lightweight-models"> <div class="section" id="lightweight-models">
<h3>Lightweight models<a class="headerlink" href="#lightweight-models" title="Permalink to this headline"></a></h3> <h3>Lightweight models<a class="headerlink" href="#lightweight-models" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p><a class="reference external" href="https://arxiv.org/abs/1707.07012">NASNet</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1707.07012">NASNet</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1801.04381">MobileNetV2</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1801.04381">MobileNetV2</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1707.01083">ShuffleNet</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1707.01083">ShuffleNet</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1807.11164">ShuffleNetV2</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1807.11164">ShuffleNetV2</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1602.07360">SqueezeNet</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1602.07360">SqueezeNet</a></li>
</ul> </ul>
</div> </div>
<div class="section" id="reid-specific-models"> <div class="section" id="reid-specific-models">
<h3>ReID-specific models<a class="headerlink" href="#reid-specific-models" title="Permalink to this headline"></a></h3> <h3>ReID-specific models<a class="headerlink" href="#reid-specific-models" title="Permalink to this headline"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p><a class="reference external" href="https://arxiv.org/abs/1709.05165">MuDeep</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1709.05165">MuDeep</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1711.08106">ResNet-mid</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1711.08106">ResNet-mid</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1802.08122">HACNN</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1802.08122">HACNN</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1711.09349">PCB</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1711.09349">PCB</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1803.09132">MLFN</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1803.09132">MLFN</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1905.00953">OSNet</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1905.00953">OSNet</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1910.06827">OSNet-AIN</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1910.06827">OSNet-AIN</a></li>
</ul> </ul>
</div> </div>
</div> </div>
<div class="section" id="losses"> <div class="section" id="losses">
<h2>Losses<a class="headerlink" href="#losses" title="Permalink to this headline"></a></h2> <h2>Losses<a class="headerlink" href="#losses" title="Permalink to this headline"></a></h2>
<ul class="simple"> <ul class="simple">
<li><p><a class="reference external" href="https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf">Softmax (cross entropy loss with label smoothing)</a></p></li> <li><a class="reference external" href="https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf">Softmax (cross entropy loss with label smoothing)</a></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/1703.07737">Triplet (hard example mining triplet loss)</a></p></li> <li><a class="reference external" href="https://arxiv.org/abs/1703.07737">Triplet (hard example mining triplet loss)</a></li>
</ul>
</div>
<div class="section" id="useful-links">
<h2>Useful links<a class="headerlink" href="#useful-links" title="Permalink to this headline"></a></h2>
<ul class="simple">
<li><a class="reference external" href="https://github.com/RodMech/OSNet-IBN1-Lite">OSNet-IBN1-Lite (test-only code with lite docker container)</a></li>
</ul> </ul>
</div> </div>
<div class="section" id="citation"> <div class="section" id="citation">
@ -447,8 +453,8 @@ test.evaluate True
<div class="section" id="indices-and-tables"> <div class="section" id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline"></a></h1> <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline"></a></h1>
<ul class="simple"> <ul class="simple">
<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li> <li><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></li>
<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li> <li><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></li>
</ul> </ul>
</div> </div>

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@ -179,70 +179,78 @@
<h2>Base Engine<a class="headerlink" href="#base-engine" title="Permalink to this headline"></a></h2> <h2>Base Engine<a class="headerlink" href="#base-engine" title="Permalink to this headline"></a></h2>
<dl class="class"> <dl class="class">
<dt id="torchreid.engine.engine.Engine"> <dt id="torchreid.engine.engine.Engine">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.engine.engine.</code><code class="sig-name descname">Engine</code><span class="sig-paren">(</span><em class="sig-param">datamanager</em>, <em class="sig-param">model</em>, <em class="sig-param">optimizer=None</em>, <em class="sig-param">scheduler=None</em>, <em class="sig-param">use_gpu=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.engine.engine.</code><code class="descname">Engine</code><span class="sig-paren">(</span><em>datamanager</em>, <em>model</em>, <em>optimizer=None</em>, <em>scheduler=None</em>, <em>use_gpu=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine" title="Permalink to this definition"></a></dt>
<dd><p>A generic base Engine class for both image- and video-reid.</p> <dd><p>A generic base Engine class for both image- and video-reid.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code> <tbody valign="top">
or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>model</strong> (<em>nn.Module</em>) – model instance.</p></li> <li><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code>
<li><p><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</p></li> or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</li>
<li><p><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</p></li> <li><strong>model</strong> (<em>nn.Module</em>) – model instance.</li>
<li><p><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use gpu. Default is True.</p></li> <li><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</li>
<li><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</li>
<li><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use gpu. Default is True.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
<dl class="method"> <dl class="method">
<dt id="torchreid.engine.engine.Engine.run"> <dt id="torchreid.engine.engine.Engine.run">
<code class="sig-name descname">run</code><span class="sig-paren">(</span><em class="sig-param">save_dir='log', max_epoch=0, start_epoch=0, fixbase_epoch=0, open_layers=None, start_eval=0, eval_freq=-1, test_only=False, print_freq=10, dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10, use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False, visactmap=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.run"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.run" title="Permalink to this definition"></a></dt> <code class="descname">run</code><span class="sig-paren">(</span><em>save_dir='log', max_epoch=0, start_epoch=0, fixbase_epoch=0, open_layers=None, start_eval=0, eval_freq=-1, test_only=False, print_freq=10, dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10, use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False, visactmap=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.run"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.run" title="Permalink to this definition"></a></dt>
<dd><p>A unified pipeline for training and evaluating a model.</p> <dd><p>A unified pipeline for training and evaluating a model.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>save_dir</strong> (<em>str</em>) – directory to save model.</p></li> <tbody valign="top">
<li><p><strong>max_epoch</strong> (<em>int</em>) – maximum epoch.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>start_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – starting epoch. Default is 0.</p></li> <li><strong>save_dir</strong> (<em>str</em>) – directory to save model.</li>
<li><p><strong>fixbase_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – number of epochs to train <code class="docutils literal notranslate"><span class="pre">open_layers</span></code> (new layers) <li><strong>max_epoch</strong> (<em>int</em>) – maximum epoch.</li>
<li><strong>start_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – starting epoch. Default is 0.</li>
<li><strong>fixbase_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – number of epochs to train <code class="docutils literal notranslate"><span class="pre">open_layers</span></code> (new layers)
while keeping base layers frozen. Default is 0. <code class="docutils literal notranslate"><span class="pre">fixbase_epoch</span></code> is counted while keeping base layers frozen. Default is 0. <code class="docutils literal notranslate"><span class="pre">fixbase_epoch</span></code> is counted
in <code class="docutils literal notranslate"><span class="pre">max_epoch</span></code>.</p></li> in <code class="docutils literal notranslate"><span class="pre">max_epoch</span></code>.</li>
<li><p><strong>open_layers</strong> (<em>str</em><em> or </em><em>list</em><em>, </em><em>optional</em>) – layers (attribute names) open for training.</p></li> <li><strong>open_layers</strong> (<em>str</em><em> or </em><em>list</em><em>, </em><em>optional</em>) – layers (attribute names) open for training.</li>
<li><p><strong>start_eval</strong> (<em>int</em><em>, </em><em>optional</em>) – from which epoch to start evaluation. Default is 0.</p></li> <li><strong>start_eval</strong> (<em>int</em><em>, </em><em>optional</em>) – from which epoch to start evaluation. Default is 0.</li>
<li><p><strong>eval_freq</strong> (<em>int</em><em>, </em><em>optional</em>) – evaluation frequency. Default is -1 (meaning evaluation <li><strong>eval_freq</strong> (<em>int</em><em>, </em><em>optional</em>) – evaluation frequency. Default is -1 (meaning evaluation
is only performed at the end of training).</p></li> is only performed at the end of training).</li>
<li><p><strong>test_only</strong> (<em>bool</em><em>, </em><em>optional</em>) – if True, only runs evaluation on test datasets. <li><strong>test_only</strong> (<em>bool</em><em>, </em><em>optional</em>) – if True, only runs evaluation on test datasets.
Default is False.</p></li> Default is False.</li>
<li><p><strong>print_freq</strong> (<em>int</em><em>, </em><em>optional</em>) – print_frequency. Default is 10.</p></li> <li><strong>print_freq</strong> (<em>int</em><em>, </em><em>optional</em>) – print_frequency. Default is 10.</li>
<li><p><strong>dist_metric</strong> (<em>str</em><em>, </em><em>optional</em>) – distance metric used to compute distance matrix <li><strong>dist_metric</strong> (<em>str</em><em>, </em><em>optional</em>) – distance metric used to compute distance matrix
between query and gallery. Default is “euclidean”.</p></li> between query and gallery. Default is “euclidean”.</li>
<li><p><strong>normalize_feature</strong> (<em>bool</em><em>, </em><em>optional</em>) – performs L2 normalization on feature vectors before <li><strong>normalize_feature</strong> (<em>bool</em><em>, </em><em>optional</em>) – performs L2 normalization on feature vectors before
computing feature distance. Default is False.</p></li> computing feature distance. Default is False.</li>
<li><p><strong>visrank</strong> (<em>bool</em><em>, </em><em>optional</em>) – visualizes ranked results. Default is False. It is recommended to <li><strong>visrank</strong> (<em>bool</em><em>, </em><em>optional</em>) – visualizes ranked results. Default is False. It is recommended to
enable <code class="docutils literal notranslate"><span class="pre">visrank</span></code> when <code class="docutils literal notranslate"><span class="pre">test_only</span></code> is True. The ranked images will be saved to enable <code class="docutils literal notranslate"><span class="pre">visrank</span></code> when <code class="docutils literal notranslate"><span class="pre">test_only</span></code> is True. The ranked images will be saved to
“save_dir/visrank_dataset”, e.g. “save_dir/visrank_market1501”.</p></li> “save_dir/visrank_dataset”, e.g. “save_dir/visrank_market1501”.</li>
<li><p><strong>visrank_topk</strong> (<em>int</em><em>, </em><em>optional</em>) – top-k ranked images to be visualized. Default is 10.</p></li> <li><strong>visrank_topk</strong> (<em>int</em><em>, </em><em>optional</em>) – top-k ranked images to be visualized. Default is 10.</li>
<li><p><strong>use_metric_cuhk03</strong> (<em>bool</em><em>, </em><em>optional</em>) – use single-gallery-shot setting for cuhk03. <li><strong>use_metric_cuhk03</strong> (<em>bool</em><em>, </em><em>optional</em>) – use single-gallery-shot setting for cuhk03.
Default is False. This should be enabled when using cuhk03 classic split.</p></li> Default is False. This should be enabled when using cuhk03 classic split.</li>
<li><p><strong>ranks</strong> (<em>list</em><em>, </em><em>optional</em>) – cmc ranks to be computed. Default is [1, 5, 10, 20].</p></li> <li><strong>ranks</strong> (<em>list</em><em>, </em><em>optional</em>) – cmc ranks to be computed. Default is [1, 5, 10, 20].</li>
<li><p><strong>rerank</strong> (<em>bool</em><em>, </em><em>optional</em>) – uses person re-ranking (by Zhong et al. CVPR’17). <li><strong>rerank</strong> (<em>bool</em><em>, </em><em>optional</em>) – uses person re-ranking (by Zhong et al. CVPR‘17).
Default is False. This is only enabled when test_only=True.</p></li> Default is False. This is only enabled when test_only=True.</li>
<li><p><strong>visactmap</strong> (<em>bool</em><em>, </em><em>optional</em>) – visualizes activation maps. Default is False.</p></li> <li><strong>visactmap</strong> (<em>bool</em><em>, </em><em>optional</em>) – visualizes activation maps. Default is False.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
</dd></dl> </dd></dl>
<dl class="method"> <dl class="method">
<dt id="torchreid.engine.engine.Engine.test"> <dt id="torchreid.engine.engine.Engine.test">
<code class="sig-name descname">test</code><span class="sig-paren">(</span><em class="sig-param">epoch, testloader, dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.test"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.test" title="Permalink to this definition"></a></dt> <code class="descname">test</code><span class="sig-paren">(</span><em>epoch, testloader, dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.test"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.test" title="Permalink to this definition"></a></dt>
<dd><p>Tests model on target datasets.</p> <dd><p>Tests model on target datasets.</p>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>This function has been called in <code class="docutils literal notranslate"><span class="pre">run()</span></code>.</p> <p class="last">This function has been called in <code class="docutils literal notranslate"><span class="pre">run()</span></code>.</p>
</div> </div>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>The test pipeline implemented in this function suits both image- and <p class="last">The test pipeline implemented in this function suits both image- and
video-reid. In general, a subclass of Engine only needs to re-implement video-reid. In general, a subclass of Engine only needs to re-implement
<code class="docutils literal notranslate"><span class="pre">_extract_features()</span></code> and <code class="docutils literal notranslate"><span class="pre">_parse_data_for_eval()</span></code> (most of the time), <code class="docutils literal notranslate"><span class="pre">_extract_features()</span></code> and <code class="docutils literal notranslate"><span class="pre">_parse_data_for_eval()</span></code> (most of the time),
but not a must. Please refer to the source code for more details.</p> but not a must. Please refer to the source code for more details.</p>
@ -251,7 +259,7 @@ but not a must. Please refer to the source code for more details.</p>
<dl class="method"> <dl class="method">
<dt id="torchreid.engine.engine.Engine.train"> <dt id="torchreid.engine.engine.Engine.train">
<code class="sig-name descname">train</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.train" title="Permalink to this definition"></a></dt> <code class="descname">train</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.train" title="Permalink to this definition"></a></dt>
<dd><p>Performs training on source datasets for one epoch.</p> <dd><p>Performs training on source datasets for one epoch.</p>
<p>This will be called every epoch in <code class="docutils literal notranslate"><span class="pre">run()</span></code>, e.g.</p> <p>This will be called every epoch in <code class="docutils literal notranslate"><span class="pre">run()</span></code>, e.g.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start_epoch</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">):</span> <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start_epoch</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">):</span>
@ -259,21 +267,22 @@ but not a must. Please refer to the source code for more details.</p>
</pre></div> </pre></div>
</div> </div>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>This must be implemented in subclasses.</p> <p class="last">This must be implemented in subclasses.</p>
</div> </div>
</dd></dl> </dd></dl>
<dl class="method"> <dl class="method">
<dt id="torchreid.engine.engine.Engine.visactmap"> <dt id="torchreid.engine.engine.Engine.visactmap">
<code class="sig-name descname">visactmap</code><span class="sig-paren">(</span><em class="sig-param">testloader</em>, <em class="sig-param">save_dir</em>, <em class="sig-param">width</em>, <em class="sig-param">height</em>, <em class="sig-param">print_freq</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.visactmap"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.visactmap" title="Permalink to this definition"></a></dt> <code class="descname">visactmap</code><span class="sig-paren">(</span><em>testloader</em>, <em>save_dir</em>, <em>width</em>, <em>height</em>, <em>print_freq</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.visactmap"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.visactmap" title="Permalink to this definition"></a></dt>
<dd><p>Visualizes CNN activation maps to see where the CNN focuses on to extract features.</p> <dd><p>Visualizes CNN activation maps to see where the CNN focuses on to extract features.</p>
<p>This function takes as input the query images of target datasets</p> <p>This function takes as input the query images of target datasets</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><ul class="simple"> <dt>Reference:</dt>
<li><p>Zagoruyko and Komodakis. Paying more attention to attention: Improving the <dd><ul class="first last simple">
performance of convolutional neural networks via attention transfer. ICLR, 2017</p></li> <li>Zagoruyko and Komodakis. Paying more attention to attention: Improving the
<li><p>Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.</p></li> performance of convolutional neural networks via attention transfer. ICLR, 2017</li>
<li>Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -286,21 +295,25 @@ performance of convolutional neural networks via attention transfer. ICLR, 2017<
<h2>Image Engines<a class="headerlink" href="#image-engines" title="Permalink to this headline"></a></h2> <h2>Image Engines<a class="headerlink" href="#image-engines" title="Permalink to this headline"></a></h2>
<dl class="class"> <dl class="class">
<dt id="torchreid.engine.image.softmax.ImageSoftmaxEngine"> <dt id="torchreid.engine.image.softmax.ImageSoftmaxEngine">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.engine.image.softmax.</code><code class="sig-name descname">ImageSoftmaxEngine</code><span class="sig-paren">(</span><em class="sig-param">datamanager</em>, <em class="sig-param">model</em>, <em class="sig-param">optimizer</em>, <em class="sig-param">scheduler=None</em>, <em class="sig-param">use_gpu=True</em>, <em class="sig-param">label_smooth=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/softmax.html#ImageSoftmaxEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.softmax.ImageSoftmaxEngine" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.engine.image.softmax.</code><code class="descname">ImageSoftmaxEngine</code><span class="sig-paren">(</span><em>datamanager</em>, <em>model</em>, <em>optimizer</em>, <em>scheduler=None</em>, <em>use_gpu=True</em>, <em>label_smooth=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/softmax.html#ImageSoftmaxEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.softmax.ImageSoftmaxEngine" title="Permalink to this definition"></a></dt>
<dd><p>Softmax-loss engine for image-reid.</p> <dd><p>Softmax-loss engine for image-reid.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code> <tbody valign="top">
or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>model</strong> (<em>nn.Module</em>) – model instance.</p></li> <li><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code>
<li><p><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</p></li> or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</li>
<li><p><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</p></li> <li><strong>model</strong> (<em>nn.Module</em>) – model instance.</li>
<li><p><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use gpu. Default is True.</p></li> <li><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</li>
<li><p><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</p></li> <li><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</li>
<li><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use gpu. Default is True.</li>
<li><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
<p>Examples:</p> <p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchreid</span> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchreid</span>
<span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ImageDataManager</span><span class="p">(</span> <span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ImageDataManager</span><span class="p">(</span>
@ -337,7 +350,7 @@ or <code class="docutils literal notranslate"><span class="pre">torchreid.data.V
</div> </div>
<dl class="method"> <dl class="method">
<dt id="torchreid.engine.image.softmax.ImageSoftmaxEngine.train"> <dt id="torchreid.engine.image.softmax.ImageSoftmaxEngine.train">
<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param">epoch</em>, <em class="sig-param">max_epoch</em>, <em class="sig-param">trainloader</em>, <em class="sig-param">fixbase_epoch=0</em>, <em class="sig-param">open_layers=None</em>, <em class="sig-param">print_freq=10</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/softmax.html#ImageSoftmaxEngine.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.softmax.ImageSoftmaxEngine.train" title="Permalink to this definition"></a></dt> <code class="descname">train</code><span class="sig-paren">(</span><em>epoch</em>, <em>max_epoch</em>, <em>trainloader</em>, <em>fixbase_epoch=0</em>, <em>open_layers=None</em>, <em>print_freq=10</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/softmax.html#ImageSoftmaxEngine.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.softmax.ImageSoftmaxEngine.train" title="Permalink to this definition"></a></dt>
<dd><p>Performs training on source datasets for one epoch.</p> <dd><p>Performs training on source datasets for one epoch.</p>
<p>This will be called every epoch in <code class="docutils literal notranslate"><span class="pre">run()</span></code>, e.g.</p> <p>This will be called every epoch in <code class="docutils literal notranslate"><span class="pre">run()</span></code>, e.g.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start_epoch</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">):</span> <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start_epoch</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">):</span>
@ -345,8 +358,8 @@ or <code class="docutils literal notranslate"><span class="pre">torchreid.data.V
</pre></div> </pre></div>
</div> </div>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>This must be implemented in subclasses.</p> <p class="last">This must be implemented in subclasses.</p>
</div> </div>
</dd></dl> </dd></dl>
@ -354,24 +367,28 @@ or <code class="docutils literal notranslate"><span class="pre">torchreid.data.V
<dl class="class"> <dl class="class">
<dt id="torchreid.engine.image.triplet.ImageTripletEngine"> <dt id="torchreid.engine.image.triplet.ImageTripletEngine">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.engine.image.triplet.</code><code class="sig-name descname">ImageTripletEngine</code><span class="sig-paren">(</span><em class="sig-param">datamanager</em>, <em class="sig-param">model</em>, <em class="sig-param">optimizer</em>, <em class="sig-param">margin=0.3</em>, <em class="sig-param">weight_t=1</em>, <em class="sig-param">weight_x=1</em>, <em class="sig-param">scheduler=None</em>, <em class="sig-param">use_gpu=True</em>, <em class="sig-param">label_smooth=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/triplet.html#ImageTripletEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.triplet.ImageTripletEngine" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.engine.image.triplet.</code><code class="descname">ImageTripletEngine</code><span class="sig-paren">(</span><em>datamanager</em>, <em>model</em>, <em>optimizer</em>, <em>margin=0.3</em>, <em>weight_t=1</em>, <em>weight_x=1</em>, <em>scheduler=None</em>, <em>use_gpu=True</em>, <em>label_smooth=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/triplet.html#ImageTripletEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.triplet.ImageTripletEngine" title="Permalink to this definition"></a></dt>
<dd><p>Triplet-loss engine for image-reid.</p> <dd><p>Triplet-loss engine for image-reid.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code> <tbody valign="top">
or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>model</strong> (<em>nn.Module</em>) – model instance.</p></li> <li><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code>
<li><p><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</p></li> or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</li>
<li><p><strong>margin</strong> (<em>float</em><em>, </em><em>optional</em>) – margin for triplet loss. Default is 0.3.</p></li> <li><strong>model</strong> (<em>nn.Module</em>) – model instance.</li>
<li><p><strong>weight_t</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for triplet loss. Default is 1.</p></li> <li><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</li>
<li><p><strong>weight_x</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for softmax loss. Default is 1.</p></li> <li><strong>margin</strong> (<em>float</em><em>, </em><em>optional</em>) – margin for triplet loss. Default is 0.3.</li>
<li><p><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</p></li> <li><strong>weight_t</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for triplet loss. Default is 1.</li>
<li><p><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use gpu. Default is True.</p></li> <li><strong>weight_x</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for softmax loss. Default is 1.</li>
<li><p><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</p></li> <li><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</li>
<li><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use gpu. Default is True.</li>
<li><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
<p>Examples:</p> <p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchreid</span> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchreid</span>
<span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ImageDataManager</span><span class="p">(</span> <span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ImageDataManager</span><span class="p">(</span>
@ -411,7 +428,7 @@ or <code class="docutils literal notranslate"><span class="pre">torchreid.data.V
</div> </div>
<dl class="method"> <dl class="method">
<dt id="torchreid.engine.image.triplet.ImageTripletEngine.train"> <dt id="torchreid.engine.image.triplet.ImageTripletEngine.train">
<code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param">epoch</em>, <em class="sig-param">max_epoch</em>, <em class="sig-param">trainloader</em>, <em class="sig-param">fixbase_epoch=0</em>, <em class="sig-param">open_layers=None</em>, <em class="sig-param">print_freq=10</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/triplet.html#ImageTripletEngine.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.triplet.ImageTripletEngine.train" title="Permalink to this definition"></a></dt> <code class="descname">train</code><span class="sig-paren">(</span><em>epoch</em>, <em>max_epoch</em>, <em>trainloader</em>, <em>fixbase_epoch=0</em>, <em>open_layers=None</em>, <em>print_freq=10</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/triplet.html#ImageTripletEngine.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.triplet.ImageTripletEngine.train" title="Permalink to this definition"></a></dt>
<dd><p>Performs training on source datasets for one epoch.</p> <dd><p>Performs training on source datasets for one epoch.</p>
<p>This will be called every epoch in <code class="docutils literal notranslate"><span class="pre">run()</span></code>, e.g.</p> <p>This will be called every epoch in <code class="docutils literal notranslate"><span class="pre">run()</span></code>, e.g.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start_epoch</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">):</span> <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start_epoch</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">):</span>
@ -419,8 +436,8 @@ or <code class="docutils literal notranslate"><span class="pre">torchreid.data.V
</pre></div> </pre></div>
</div> </div>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>This must be implemented in subclasses.</p> <p class="last">This must be implemented in subclasses.</p>
</div> </div>
</dd></dl> </dd></dl>
@ -431,23 +448,27 @@ or <code class="docutils literal notranslate"><span class="pre">torchreid.data.V
<h2>Video Engines<a class="headerlink" href="#video-engines" title="Permalink to this headline"></a></h2> <h2>Video Engines<a class="headerlink" href="#video-engines" title="Permalink to this headline"></a></h2>
<dl class="class"> <dl class="class">
<dt id="torchreid.engine.video.softmax.VideoSoftmaxEngine"> <dt id="torchreid.engine.video.softmax.VideoSoftmaxEngine">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.engine.video.softmax.</code><code class="sig-name descname">VideoSoftmaxEngine</code><span class="sig-paren">(</span><em class="sig-param">datamanager</em>, <em class="sig-param">model</em>, <em class="sig-param">optimizer</em>, <em class="sig-param">scheduler=None</em>, <em class="sig-param">use_gpu=True</em>, <em class="sig-param">label_smooth=True</em>, <em class="sig-param">pooling_method='avg'</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/video/softmax.html#VideoSoftmaxEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.video.softmax.VideoSoftmaxEngine" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.engine.video.softmax.</code><code class="descname">VideoSoftmaxEngine</code><span class="sig-paren">(</span><em>datamanager</em>, <em>model</em>, <em>optimizer</em>, <em>scheduler=None</em>, <em>use_gpu=True</em>, <em>label_smooth=True</em>, <em>pooling_method='avg'</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/video/softmax.html#VideoSoftmaxEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.video.softmax.VideoSoftmaxEngine" title="Permalink to this definition"></a></dt>
<dd><p>Softmax-loss engine for video-reid.</p> <dd><p>Softmax-loss engine for video-reid.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code> <tbody valign="top">
or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>model</strong> (<em>nn.Module</em>) – model instance.</p></li> <li><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code>
<li><p><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</p></li> or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</li>
<li><p><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</p></li> <li><strong>model</strong> (<em>nn.Module</em>) – model instance.</li>
<li><p><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use gpu. Default is True.</p></li> <li><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</li>
<li><p><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</p></li> <li><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</li>
<li><p><strong>pooling_method</strong> (<em>str</em><em>, </em><em>optional</em>) – how to pool features for a tracklet. <li><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use gpu. Default is True.</li>
Default is “avg” (average). Choices are [“avg”, “max”].</p></li> <li><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</li>
<li><strong>pooling_method</strong> (<em>str</em><em>, </em><em>optional</em>) – how to pool features for a tracklet.
Default is “avg” (average). Choices are [“avg”, “max”].</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
<p>Examples:</p> <p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchreid</span> <span class="kn">import</span> <span class="nn">torchreid</span>
@ -490,26 +511,30 @@ Default is “avg” (average). Choices are [“avg”, “max”].</p></li>
<dl class="class"> <dl class="class">
<dt id="torchreid.engine.video.triplet.VideoTripletEngine"> <dt id="torchreid.engine.video.triplet.VideoTripletEngine">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.engine.video.triplet.</code><code class="sig-name descname">VideoTripletEngine</code><span class="sig-paren">(</span><em class="sig-param">datamanager</em>, <em class="sig-param">model</em>, <em class="sig-param">optimizer</em>, <em class="sig-param">margin=0.3</em>, <em class="sig-param">weight_t=1</em>, <em class="sig-param">weight_x=1</em>, <em class="sig-param">scheduler=None</em>, <em class="sig-param">use_gpu=True</em>, <em class="sig-param">label_smooth=True</em>, <em class="sig-param">pooling_method='avg'</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/video/triplet.html#VideoTripletEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.video.triplet.VideoTripletEngine" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.engine.video.triplet.</code><code class="descname">VideoTripletEngine</code><span class="sig-paren">(</span><em>datamanager</em>, <em>model</em>, <em>optimizer</em>, <em>margin=0.3</em>, <em>weight_t=1</em>, <em>weight_x=1</em>, <em>scheduler=None</em>, <em>use_gpu=True</em>, <em>label_smooth=True</em>, <em>pooling_method='avg'</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/video/triplet.html#VideoTripletEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.video.triplet.VideoTripletEngine" title="Permalink to this definition"></a></dt>
<dd><p>Triplet-loss engine for video-reid.</p> <dd><p>Triplet-loss engine for video-reid.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code> <tbody valign="top">
or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>model</strong> (<em>nn.Module</em>) – model instance.</p></li> <li><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code>
<li><p><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</p></li> or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</li>
<li><p><strong>margin</strong> (<em>float</em><em>, </em><em>optional</em>) – margin for triplet loss. Default is 0.3.</p></li> <li><strong>model</strong> (<em>nn.Module</em>) – model instance.</li>
<li><p><strong>weight_t</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for triplet loss. Default is 1.</p></li> <li><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</li>
<li><p><strong>weight_x</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for softmax loss. Default is 1.</p></li> <li><strong>margin</strong> (<em>float</em><em>, </em><em>optional</em>) – margin for triplet loss. Default is 0.3.</li>
<li><p><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</p></li> <li><strong>weight_t</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for triplet loss. Default is 1.</li>
<li><p><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use gpu. Default is True.</p></li> <li><strong>weight_x</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for softmax loss. Default is 1.</li>
<li><p><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</p></li> <li><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</li>
<li><p><strong>pooling_method</strong> (<em>str</em><em>, </em><em>optional</em>) – how to pool features for a tracklet. <li><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use gpu. Default is True.</li>
Default is “avg” (average). Choices are [“avg”, “max”].</p></li> <li><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</li>
<li><strong>pooling_method</strong> (<em>str</em><em>, </em><em>optional</em>) – how to pool features for a tracklet.
Default is “avg” (average). Choices are [“avg”, “max”].</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
<p>Examples:</p> <p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchreid</span> <span class="kn">import</span> <span class="nn">torchreid</span>

95
pkg/losses.html
View File

@ -178,11 +178,11 @@
<span id="softmax"></span><h2>Softmax<a class="headerlink" href="#module-torchreid.losses.cross_entropy_loss" title="Permalink to this headline"></a></h2> <span id="softmax"></span><h2>Softmax<a class="headerlink" href="#module-torchreid.losses.cross_entropy_loss" title="Permalink to this headline"></a></h2>
<dl class="class"> <dl class="class">
<dt id="torchreid.losses.cross_entropy_loss.CrossEntropyLoss"> <dt id="torchreid.losses.cross_entropy_loss.CrossEntropyLoss">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.losses.cross_entropy_loss.</code><code class="sig-name descname">CrossEntropyLoss</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">epsilon=0.1</em>, <em class="sig-param">use_gpu=True</em>, <em class="sig-param">label_smooth=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/losses/cross_entropy_loss.html#CrossEntropyLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.losses.cross_entropy_loss.CrossEntropyLoss" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.losses.cross_entropy_loss.</code><code class="descname">CrossEntropyLoss</code><span class="sig-paren">(</span><em>num_classes</em>, <em>epsilon=0.1</em>, <em>use_gpu=True</em>, <em>label_smooth=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/losses/cross_entropy_loss.html#CrossEntropyLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.losses.cross_entropy_loss.CrossEntropyLoss" title="Permalink to this definition"></a></dt>
<dd><p>Cross entropy loss with label smoothing regularizer.</p> <dd><p>Cross entropy loss with label smoothing regularizer.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.</p> <dt>Reference:</dt>
</dd> <dd>Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.</dd>
</dl> </dl>
<p>With label smoothing, the label <span class="math notranslate nohighlight">\(y\)</span> for a class is computed by</p> <p>With label smoothing, the label <span class="math notranslate nohighlight">\(y\)</span> for a class is computed by</p>
<div class="math notranslate nohighlight"> <div class="math notranslate nohighlight">
@ -191,29 +191,37 @@
\end{equation}\]</div> \end{equation}\]</div>
<p>where <span class="math notranslate nohighlight">\(K\)</span> denotes the number of classes and <span class="math notranslate nohighlight">\(\epsilon\)</span> is a weight. When <p>where <span class="math notranslate nohighlight">\(K\)</span> denotes the number of classes and <span class="math notranslate nohighlight">\(\epsilon\)</span> is a weight. When
<span class="math notranslate nohighlight">\(\epsilon = 0\)</span>, the loss function reduces to the normal cross entropy.</p> <span class="math notranslate nohighlight">\(\epsilon = 0\)</span>, the loss function reduces to the normal cross entropy.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>num_classes</strong> (<em>int</em>) – number of classes.</p></li> <tbody valign="top">
<li><p><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – weight. Default is 0.1.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to use gpu devices. Default is True.</p></li> <li><strong>num_classes</strong> (<em>int</em>) – number of classes.</li>
<li><p><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to apply label smoothing. Default is True.</p></li> <li><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – weight. Default is 0.1.</li>
<li><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to use gpu devices. Default is True.</li>
<li><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to apply label smoothing. Default is True.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
<dl class="method"> <dl class="method">
<dt id="torchreid.losses.cross_entropy_loss.CrossEntropyLoss.forward"> <dt id="torchreid.losses.cross_entropy_loss.CrossEntropyLoss.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">targets</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/losses/cross_entropy_loss.html#CrossEntropyLoss.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.losses.cross_entropy_loss.CrossEntropyLoss.forward" title="Permalink to this definition"></a></dt> <code class="descname">forward</code><span class="sig-paren">(</span><em>inputs</em>, <em>targets</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/losses/cross_entropy_loss.html#CrossEntropyLoss.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.losses.cross_entropy_loss.CrossEntropyLoss.forward" title="Permalink to this definition"></a></dt>
<dd><dl class="field-list simple"> <dd><table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>inputs</strong> (<em>torch.Tensor</em>) – prediction matrix (before softmax) with <tbody valign="top">
shape (batch_size, num_classes).</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>targets</strong> (<em>torch.LongTensor</em>) – ground truth labels with shape (batch_size). <li><strong>inputs</strong> (<em>torch.Tensor</em>) – prediction matrix (before softmax) with
Each position contains the label index.</p></li> shape (batch_size, num_classes).</li>
<li><strong>targets</strong> (<em>torch.LongTensor</em>) – ground truth labels with shape (batch_size).
Each position contains the label index.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
</dd></dl> </dd></dl>
</dd></dl> </dd></dl>
@ -223,29 +231,36 @@ Each position contains the label index.</p></li>
<span id="triplet"></span><h2>Triplet<a class="headerlink" href="#module-torchreid.losses.hard_mine_triplet_loss" title="Permalink to this headline"></a></h2> <span id="triplet"></span><h2>Triplet<a class="headerlink" href="#module-torchreid.losses.hard_mine_triplet_loss" title="Permalink to this headline"></a></h2>
<dl class="class"> <dl class="class">
<dt id="torchreid.losses.hard_mine_triplet_loss.TripletLoss"> <dt id="torchreid.losses.hard_mine_triplet_loss.TripletLoss">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.losses.hard_mine_triplet_loss.</code><code class="sig-name descname">TripletLoss</code><span class="sig-paren">(</span><em class="sig-param">margin=0.3</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/losses/hard_mine_triplet_loss.html#TripletLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.losses.hard_mine_triplet_loss.TripletLoss" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.losses.hard_mine_triplet_loss.</code><code class="descname">TripletLoss</code><span class="sig-paren">(</span><em>margin=0.3</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/losses/hard_mine_triplet_loss.html#TripletLoss"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.losses.hard_mine_triplet_loss.TripletLoss" title="Permalink to this definition"></a></dt>
<dd><p>Triplet loss with hard positive/negative mining.</p> <dd><p>Triplet loss with hard positive/negative mining.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.</p> <dt>Reference:</dt>
</dd> <dd>Hermans et al. In Defense of the Triplet Loss for Person Re-Identification. arXiv:1703.07737.</dd>
</dl> </dl>
<p>Imported from <a class="reference external" href="https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py">https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py</a>.</p> <p>Imported from <a class="reference external" href="https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py">https://github.com/Cysu/open-reid/blob/master/reid/loss/triplet.py</a>.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><p><strong>margin</strong> (<em>float</em><em>, </em><em>optional</em>) – margin for triplet. Default is 0.3.</p> <col class="field-body" />
</dd> <tbody valign="top">
</dl> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>margin</strong> (<em>float</em><em>, </em><em>optional</em>) – margin for triplet. Default is 0.3.</td>
</tr>
</tbody>
</table>
<dl class="method"> <dl class="method">
<dt id="torchreid.losses.hard_mine_triplet_loss.TripletLoss.forward"> <dt id="torchreid.losses.hard_mine_triplet_loss.TripletLoss.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">inputs</em>, <em class="sig-param">targets</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/losses/hard_mine_triplet_loss.html#TripletLoss.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.losses.hard_mine_triplet_loss.TripletLoss.forward" title="Permalink to this definition"></a></dt> <code class="descname">forward</code><span class="sig-paren">(</span><em>inputs</em>, <em>targets</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/losses/hard_mine_triplet_loss.html#TripletLoss.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.losses.hard_mine_triplet_loss.TripletLoss.forward" title="Permalink to this definition"></a></dt>
<dd><dl class="field-list simple"> <dd><table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>inputs</strong> (<em>torch.Tensor</em>) – feature matrix with shape (batch_size, feat_dim).</p></li> <tbody valign="top">
<li><p><strong>targets</strong> (<em>torch.LongTensor</em>) – ground truth labels with shape (num_classes).</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>inputs</strong> (<em>torch.Tensor</em>) – feature matrix with shape (batch_size, feat_dim).</li>
<li><strong>targets</strong> (<em>torch.LongTensor</em>) – ground truth labels with shape (num_classes).</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
</dd></dl> </dd></dl>
</dd></dl> </dd></dl>

190
pkg/metrics.html
View File

@ -179,26 +179,31 @@
<span id="distance"></span><h2>Distance<a class="headerlink" href="#module-torchreid.metrics.distance" title="Permalink to this headline"></a></h2> <span id="distance"></span><h2>Distance<a class="headerlink" href="#module-torchreid.metrics.distance" title="Permalink to this headline"></a></h2>
<dl class="function"> <dl class="function">
<dt id="torchreid.metrics.distance.compute_distance_matrix"> <dt id="torchreid.metrics.distance.compute_distance_matrix">
<code class="sig-prename descclassname">torchreid.metrics.distance.</code><code class="sig-name descname">compute_distance_matrix</code><span class="sig-paren">(</span><em class="sig-param">input1</em>, <em class="sig-param">input2</em>, <em class="sig-param">metric='euclidean'</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/metrics/distance.html#compute_distance_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.metrics.distance.compute_distance_matrix" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.metrics.distance.</code><code class="descname">compute_distance_matrix</code><span class="sig-paren">(</span><em>input1</em>, <em>input2</em>, <em>metric='euclidean'</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/metrics/distance.html#compute_distance_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.metrics.distance.compute_distance_matrix" title="Permalink to this definition"></a></dt>
<dd><p>A wrapper function for computing distance matrix.</p> <dd><p>A wrapper function for computing distance matrix.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>input1</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</p></li> <tbody valign="top">
<li><p><strong>input2</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><p><strong>metric</strong> (<em>str</em><em>, </em><em>optional</em>) – “euclidean” or “cosine”. <li><strong>input1</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</li>
Default is “euclidean”.</p></li> <li><strong>input2</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</li>
<li><strong>metric</strong> (<em>str</em><em>, </em><em>optional</em>) – “euclidean” or “cosine”.
Default is “euclidean”.</li>
</ul> </ul>
</dd> </td>
<dt class="field-even">Returns</dt> </tr>
<dd class="field-even"><p>distance matrix.</p> <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">distance matrix.</p>
</dd> </td>
<dt class="field-odd">Return type</dt> </tr>
<dd class="field-odd"><p>torch.Tensor</p> <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">torch.Tensor</p>
</dd> </td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">metrics</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">metrics</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">input1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2048</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">input1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2048</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">input2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">2048</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">input2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">2048</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">distmat</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">compute_distance_matrix</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">distmat</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">compute_distance_matrix</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">)</span>
@ -211,42 +216,50 @@ Default is “euclidean”.</p></li>
<dl class="function"> <dl class="function">
<dt id="torchreid.metrics.distance.cosine_distance"> <dt id="torchreid.metrics.distance.cosine_distance">
<code class="sig-prename descclassname">torchreid.metrics.distance.</code><code class="sig-name descname">cosine_distance</code><span class="sig-paren">(</span><em class="sig-param">input1</em>, <em class="sig-param">input2</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/metrics/distance.html#cosine_distance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.metrics.distance.cosine_distance" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.metrics.distance.</code><code class="descname">cosine_distance</code><span class="sig-paren">(</span><em>input1</em>, <em>input2</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/metrics/distance.html#cosine_distance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.metrics.distance.cosine_distance" title="Permalink to this definition"></a></dt>
<dd><p>Computes cosine distance.</p> <dd><p>Computes cosine distance.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>input1</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</p></li> <tbody valign="top">
<li><p><strong>input2</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input1</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</li>
<li><strong>input2</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</li>
</ul> </ul>
</dd> </td>
<dt class="field-even">Returns</dt> </tr>
<dd class="field-even"><p>distance matrix.</p> <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">distance matrix.</p>
</dd> </td>
<dt class="field-odd">Return type</dt> </tr>
<dd class="field-odd"><p>torch.Tensor</p> <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">torch.Tensor</p>
</dd> </td>
</dl> </tr>
</tbody>
</table>
</dd></dl> </dd></dl>
<dl class="function"> <dl class="function">
<dt id="torchreid.metrics.distance.euclidean_squared_distance"> <dt id="torchreid.metrics.distance.euclidean_squared_distance">
<code class="sig-prename descclassname">torchreid.metrics.distance.</code><code class="sig-name descname">euclidean_squared_distance</code><span class="sig-paren">(</span><em class="sig-param">input1</em>, <em class="sig-param">input2</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/metrics/distance.html#euclidean_squared_distance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.metrics.distance.euclidean_squared_distance" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.metrics.distance.</code><code class="descname">euclidean_squared_distance</code><span class="sig-paren">(</span><em>input1</em>, <em>input2</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/metrics/distance.html#euclidean_squared_distance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.metrics.distance.euclidean_squared_distance" title="Permalink to this definition"></a></dt>
<dd><p>Computes euclidean squared distance.</p> <dd><p>Computes euclidean squared distance.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>input1</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</p></li> <tbody valign="top">
<li><p><strong>input2</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>input1</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</li>
<li><strong>input2</strong> (<em>torch.Tensor</em>) – 2-D feature matrix.</li>
</ul> </ul>
</dd> </td>
<dt class="field-even">Returns</dt> </tr>
<dd class="field-even"><p>distance matrix.</p> <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">distance matrix.</p>
</dd> </td>
<dt class="field-odd">Return type</dt> </tr>
<dd class="field-odd"><p>torch.Tensor</p> <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">torch.Tensor</p>
</dd> </td>
</dl> </tr>
</tbody>
</table>
</dd></dl> </dd></dl>
</div> </div>
@ -254,27 +267,32 @@ Default is “euclidean”.</p></li>
<span id="accuracy"></span><h2>Accuracy<a class="headerlink" href="#module-torchreid.metrics.accuracy" title="Permalink to this headline"></a></h2> <span id="accuracy"></span><h2>Accuracy<a class="headerlink" href="#module-torchreid.metrics.accuracy" title="Permalink to this headline"></a></h2>
<dl class="function"> <dl class="function">
<dt id="torchreid.metrics.accuracy.accuracy"> <dt id="torchreid.metrics.accuracy.accuracy">
<code class="sig-prename descclassname">torchreid.metrics.accuracy.</code><code class="sig-name descname">accuracy</code><span class="sig-paren">(</span><em class="sig-param">output</em>, <em class="sig-param">target</em>, <em class="sig-param">topk=(1</em>, <em class="sig-param">)</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/metrics/accuracy.html#accuracy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.metrics.accuracy.accuracy" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.metrics.accuracy.</code><code class="descname">accuracy</code><span class="sig-paren">(</span><em>output</em>, <em>target</em>, <em>topk=(1</em>, <em>)</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/metrics/accuracy.html#accuracy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.metrics.accuracy.accuracy" title="Permalink to this definition"></a></dt>
<dd><p>Computes the accuracy over the k top predictions for <dd><p>Computes the accuracy over the k top predictions for
the specified values of k.</p> the specified values of k.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>output</strong> (<em>torch.Tensor</em>) – prediction matrix with shape (batch_size, num_classes).</p></li> <tbody valign="top">
<li><p><strong>target</strong> (<em>torch.LongTensor</em>) – ground truth labels with shape (batch_size).</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><p><strong>topk</strong> (<em>tuple</em><em>, </em><em>optional</em>) – accuracy at top-k will be computed. For example, <li><strong>output</strong> (<em>torch.Tensor</em>) – prediction matrix with shape (batch_size, num_classes).</li>
topk=(1, 5) means accuracy at top-1 and top-5 will be computed.</p></li> <li><strong>target</strong> (<em>torch.LongTensor</em>) – ground truth labels with shape (batch_size).</li>
<li><strong>topk</strong> (<em>tuple</em><em>, </em><em>optional</em>) – accuracy at top-k will be computed. For example,
topk=(1, 5) means accuracy at top-1 and top-5 will be computed.</li>
</ul> </ul>
</dd> </td>
<dt class="field-even">Returns</dt> </tr>
<dd class="field-even"><p>accuracy at top-k.</p> <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">accuracy at top-k.</p>
</dd> </td>
<dt class="field-odd">Return type</dt> </tr>
<dd class="field-odd"><p>list</p> <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">list</p>
</dd> </td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">metrics</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">metrics</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">accuracy</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">metrics</span><span class="o">.</span><span class="n">accuracy</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
</pre></div> </pre></div>
</div> </div>
@ -287,29 +305,33 @@ topk=(1, 5) means accuracy at top-1 and top-5 will be computed.</p></li>
<span id="rank"></span><h2>Rank<a class="headerlink" href="#module-torchreid.metrics.rank" title="Permalink to this headline"></a></h2> <span id="rank"></span><h2>Rank<a class="headerlink" href="#module-torchreid.metrics.rank" title="Permalink to this headline"></a></h2>
<dl class="function"> <dl class="function">
<dt id="torchreid.metrics.rank.evaluate_rank"> <dt id="torchreid.metrics.rank.evaluate_rank">
<code class="sig-prename descclassname">torchreid.metrics.rank.</code><code class="sig-name descname">evaluate_rank</code><span class="sig-paren">(</span><em class="sig-param">distmat</em>, <em class="sig-param">q_pids</em>, <em class="sig-param">g_pids</em>, <em class="sig-param">q_camids</em>, <em class="sig-param">g_camids</em>, <em class="sig-param">max_rank=50</em>, <em class="sig-param">use_metric_cuhk03=False</em>, <em class="sig-param">use_cython=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/metrics/rank.html#evaluate_rank"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.metrics.rank.evaluate_rank" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.metrics.rank.</code><code class="descname">evaluate_rank</code><span class="sig-paren">(</span><em>distmat</em>, <em>q_pids</em>, <em>g_pids</em>, <em>q_camids</em>, <em>g_camids</em>, <em>max_rank=50</em>, <em>use_metric_cuhk03=False</em>, <em>use_cython=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/metrics/rank.html#evaluate_rank"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.metrics.rank.evaluate_rank" title="Permalink to this definition"></a></dt>
<dd><p>Evaluates CMC rank.</p> <dd><p>Evaluates CMC rank.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>distmat</strong> (<em>numpy.ndarray</em>) – distance matrix of shape (num_query, num_gallery).</p></li> <tbody valign="top">
<li><p><strong>q_pids</strong> (<em>numpy.ndarray</em>) – 1-D array containing person identities <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
of each query instance.</p></li> <li><strong>distmat</strong> (<em>numpy.ndarray</em>) – distance matrix of shape (num_query, num_gallery).</li>
<li><p><strong>g_pids</strong> (<em>numpy.ndarray</em>) – 1-D array containing person identities <li><strong>q_pids</strong> (<em>numpy.ndarray</em>) – 1-D array containing person identities
of each gallery instance.</p></li> of each query instance.</li>
<li><p><strong>q_camids</strong> (<em>numpy.ndarray</em>) – 1-D array containing camera views under <li><strong>g_pids</strong> (<em>numpy.ndarray</em>) – 1-D array containing person identities
which each query instance is captured.</p></li> of each gallery instance.</li>
<li><p><strong>g_camids</strong> (<em>numpy.ndarray</em>) – 1-D array containing camera views under <li><strong>q_camids</strong> (<em>numpy.ndarray</em>) – 1-D array containing camera views under
which each gallery instance is captured.</p></li> which each query instance is captured.</li>
<li><p><strong>max_rank</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum CMC rank to be computed. Default is 50.</p></li> <li><strong>g_camids</strong> (<em>numpy.ndarray</em>) – 1-D array containing camera views under
<li><p><strong>use_metric_cuhk03</strong> (<em>bool</em><em>, </em><em>optional</em>) – use single-gallery-shot setting for cuhk03. which each gallery instance is captured.</li>
Default is False. This should be enabled when using cuhk03 classic split.</p></li> <li><strong>max_rank</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum CMC rank to be computed. Default is 50.</li>
<li><p><strong>use_cython</strong> (<em>bool</em><em>, </em><em>optional</em>) – use cython code for evaluation. Default is True. <li><strong>use_metric_cuhk03</strong> (<em>bool</em><em>, </em><em>optional</em>) – use single-gallery-shot setting for cuhk03.
Default is False. This should be enabled when using cuhk03 classic split.</li>
<li><strong>use_cython</strong> (<em>bool</em><em>, </em><em>optional</em>) – use cython code for evaluation. Default is True.
This is highly recommended as the cython code can speed up the cmc computation This is highly recommended as the cython code can speed up the cmc computation
by more than 10x. This requires Cython to be installed.</p></li> by more than 10x. This requires Cython to be installed.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
</dd></dl> </dd></dl>
</div> </div>

355
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@ -180,26 +180,31 @@
<span id="interface"></span><h2>Interface<a class="headerlink" href="#module-torchreid.models.__init__" title="Permalink to this headline"></a></h2> <span id="interface"></span><h2>Interface<a class="headerlink" href="#module-torchreid.models.__init__" title="Permalink to this headline"></a></h2>
<dl class="function"> <dl class="function">
<dt id="torchreid.models.__init__.build_model"> <dt id="torchreid.models.__init__.build_model">
<code class="sig-prename descclassname">torchreid.models.__init__.</code><code class="sig-name descname">build_model</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">num_classes</em>, <em class="sig-param">loss='softmax'</em>, <em class="sig-param">pretrained=True</em>, <em class="sig-param">use_gpu=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/__init__.html#build_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.__init__.build_model" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.models.__init__.</code><code class="descname">build_model</code><span class="sig-paren">(</span><em>name</em>, <em>num_classes</em>, <em>loss='softmax'</em>, <em>pretrained=True</em>, <em>use_gpu=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/__init__.html#build_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.__init__.build_model" title="Permalink to this definition"></a></dt>
<dd><p>A function wrapper for building a model.</p> <dd><p>A function wrapper for building a model.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>name</strong> (<em>str</em>) – model name.</p></li> <tbody valign="top">
<li><p><strong>num_classes</strong> (<em>int</em>) – number of training identities.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><p><strong>loss</strong> (<em>str</em><em>, </em><em>optional</em>) – loss function to optimize the model. Currently <li><strong>name</strong> (<em>str</em>) – model name.</li>
supports “softmax” and “triplet”. Default is “softmax”.</p></li> <li><strong>num_classes</strong> (<em>int</em>) – number of training identities.</li>
<li><p><strong>pretrained</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to load ImageNet-pretrained weights. <li><strong>loss</strong> (<em>str</em><em>, </em><em>optional</em>) – loss function to optimize the model. Currently
Default is True.</p></li> supports “softmax” and “triplet”. Default is “softmax”.</li>
<li><p><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to use gpu. Default is True.</p></li> <li><strong>pretrained</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to load ImageNet-pretrained weights.
Default is True.</li>
<li><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to use gpu. Default is True.</li>
</ul> </ul>
</dd> </td>
<dt class="field-even">Returns</dt> </tr>
<dd class="field-even"><p>nn.Module</p> <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">nn.Module</p>
</dd> </td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">models</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">models</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">build_model</span><span class="p">(</span><span class="s1">&#39;resnet50&#39;</span><span class="p">,</span> <span class="mi">751</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">build_model</span><span class="p">(</span><span class="s1">&#39;resnet50&#39;</span><span class="p">,</span> <span class="mi">751</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)</span>
</pre></div> </pre></div>
</div> </div>
@ -209,10 +214,11 @@ Default is True.</p></li>
<dl class="function"> <dl class="function">
<dt id="torchreid.models.__init__.show_avai_models"> <dt id="torchreid.models.__init__.show_avai_models">
<code class="sig-prename descclassname">torchreid.models.__init__.</code><code class="sig-name descname">show_avai_models</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/__init__.html#show_avai_models"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.__init__.show_avai_models" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.models.__init__.</code><code class="descname">show_avai_models</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/__init__.html#show_avai_models"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.__init__.show_avai_models" title="Permalink to this definition"></a></dt>
<dd><p>Displays available models.</p> <dd><p>Displays available models.</p>
<dl> <dl class="docutils">
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">models</span> <dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">models</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">models</span><span class="o">.</span><span class="n">show_avai_models</span><span class="p">()</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">models</span><span class="o">.</span><span class="n">show_avai_models</span><span class="p">()</span>
</pre></div> </pre></div>
</div> </div>
@ -225,23 +231,25 @@ Default is True.</p></li>
<h2>ImageNet Classification Models<a class="headerlink" href="#imagenet-classification-models" title="Permalink to this headline"></a></h2> <h2>ImageNet Classification Models<a class="headerlink" href="#imagenet-classification-models" title="Permalink to this headline"></a></h2>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.resnet.ResNet"> <dt id="torchreid.models.resnet.ResNet">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.resnet.</code><code class="sig-name descname">ResNet</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss</em>, <em class="sig-param">block</em>, <em class="sig-param">layers</em>, <em class="sig-param">zero_init_residual=False</em>, <em class="sig-param">groups=1</em>, <em class="sig-param">width_per_group=64</em>, <em class="sig-param">replace_stride_with_dilation=None</em>, <em class="sig-param">norm_layer=None</em>, <em class="sig-param">last_stride=2</em>, <em class="sig-param">fc_dims=None</em>, <em class="sig-param">dropout_p=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/resnet.html#ResNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.resnet.ResNet" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.resnet.</code><code class="descname">ResNet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>block</em>, <em>layers</em>, <em>zero_init_residual=False</em>, <em>groups=1</em>, <em>width_per_group=64</em>, <em>replace_stride_with_dilation=None</em>, <em>norm_layer=None</em>, <em>last_stride=2</em>, <em>fc_dims=None</em>, <em>dropout_p=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/resnet.html#ResNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.resnet.ResNet" title="Permalink to this definition"></a></dt>
<dd><p>Residual network.</p> <dd><p>Residual network.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><ul class="simple"> <dt>Reference:</dt>
<li><p>He et al. Deep Residual Learning for Image Recognition. CVPR 2016.</p></li> <dd><ul class="first last simple">
<li><p>Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.</p></li> <li>He et al. Deep Residual Learning for Image Recognition. CVPR 2016.</li>
<li>Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.</li>
</ul> </ul>
</dd> </dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">resnet18</span></code>: ResNet18.</p></li> <dd><ul class="first last simple">
<li><p><code class="docutils literal notranslate"><span class="pre">resnet34</span></code>: ResNet34.</p></li> <li><code class="docutils literal notranslate"><span class="pre">resnet18</span></code>: ResNet18.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">resnet50</span></code>: ResNet50.</p></li> <li><code class="docutils literal notranslate"><span class="pre">resnet34</span></code>: ResNet34.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">resnet101</span></code>: ResNet101.</p></li> <li><code class="docutils literal notranslate"><span class="pre">resnet50</span></code>: ResNet50.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">resnet152</span></code>: ResNet152.</p></li> <li><code class="docutils literal notranslate"><span class="pre">resnet101</span></code>: ResNet101.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">resnext50_32x4d</span></code>: ResNeXt50.</p></li> <li><code class="docutils literal notranslate"><span class="pre">resnet152</span></code>: ResNet152.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">resnext101_32x8d</span></code>: ResNeXt101.</p></li> <li><code class="docutils literal notranslate"><span class="pre">resnext50_32x4d</span></code>: ResNeXt50.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">resnet50_fc512</span></code>: ResNet50 + FC.</p></li> <li><code class="docutils literal notranslate"><span class="pre">resnext101_32x8d</span></code>: ResNeXt101.</li>
<li><code class="docutils literal notranslate"><span class="pre">resnet50_fc512</span></code>: ResNet50 + FC.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -249,19 +257,20 @@ Default is True.</p></li>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.senet.SENet"> <dt id="torchreid.models.senet.SENet">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.senet.</code><code class="sig-name descname">SENet</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss</em>, <em class="sig-param">block</em>, <em class="sig-param">layers</em>, <em class="sig-param">groups</em>, <em class="sig-param">reduction</em>, <em class="sig-param">dropout_p=0.2</em>, <em class="sig-param">inplanes=128</em>, <em class="sig-param">input_3x3=True</em>, <em class="sig-param">downsample_kernel_size=3</em>, <em class="sig-param">downsample_padding=1</em>, <em class="sig-param">last_stride=2</em>, <em class="sig-param">fc_dims=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/senet.html#SENet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.senet.SENet" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.senet.</code><code class="descname">SENet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>block</em>, <em>layers</em>, <em>groups</em>, <em>reduction</em>, <em>dropout_p=0.2</em>, <em>inplanes=128</em>, <em>input_3x3=True</em>, <em>downsample_kernel_size=3</em>, <em>downsample_padding=1</em>, <em>last_stride=2</em>, <em>fc_dims=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/senet.html#SENet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.senet.SENet" title="Permalink to this definition"></a></dt>
<dd><p>Squeeze-and-excitation network.</p> <dd><p>Squeeze-and-excitation network.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Hu et al. Squeeze-and-Excitation Networks. CVPR 2018.</p> <dt>Reference:</dt>
</dd> <dd>Hu et al. Squeeze-and-Excitation Networks. CVPR 2018.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">senet154</span></code>: SENet154.</p></li> <dd><ul class="first last simple">
<li><p><code class="docutils literal notranslate"><span class="pre">se_resnet50</span></code>: ResNet50 + SE.</p></li> <li><code class="docutils literal notranslate"><span class="pre">senet154</span></code>: SENet154.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">se_resnet101</span></code>: ResNet101 + SE.</p></li> <li><code class="docutils literal notranslate"><span class="pre">se_resnet50</span></code>: ResNet50 + SE.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">se_resnet152</span></code>: ResNet152 + SE.</p></li> <li><code class="docutils literal notranslate"><span class="pre">se_resnet101</span></code>: ResNet101 + SE.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">se_resnext50_32x4d</span></code>: ResNeXt50 (groups=32, width=4) + SE.</p></li> <li><code class="docutils literal notranslate"><span class="pre">se_resnet152</span></code>: ResNet152 + SE.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">se_resnext101_32x4d</span></code>: ResNeXt101 (groups=32, width=4) + SE.</p></li> <li><code class="docutils literal notranslate"><span class="pre">se_resnext50_32x4d</span></code>: ResNeXt50 (groups=32, width=4) + SE.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">se_resnet50_fc512</span></code>: (ResNet50 + SE) + FC.</p></li> <li><code class="docutils literal notranslate"><span class="pre">se_resnext101_32x4d</span></code>: ResNeXt101 (groups=32, width=4) + SE.</li>
<li><code class="docutils literal notranslate"><span class="pre">se_resnet50_fc512</span></code>: (ResNet50 + SE) + FC.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -269,17 +278,18 @@ Default is True.</p></li>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.densenet.DenseNet"> <dt id="torchreid.models.densenet.DenseNet">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.densenet.</code><code class="sig-name descname">DenseNet</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss</em>, <em class="sig-param">growth_rate=32</em>, <em class="sig-param">block_config=(6</em>, <em class="sig-param">12</em>, <em class="sig-param">24</em>, <em class="sig-param">16)</em>, <em class="sig-param">num_init_features=64</em>, <em class="sig-param">bn_size=4</em>, <em class="sig-param">drop_rate=0</em>, <em class="sig-param">fc_dims=None</em>, <em class="sig-param">dropout_p=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/densenet.html#DenseNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.densenet.DenseNet" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.densenet.</code><code class="descname">DenseNet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>growth_rate=32</em>, <em>block_config=(6</em>, <em>12</em>, <em>24</em>, <em>16)</em>, <em>num_init_features=64</em>, <em>bn_size=4</em>, <em>drop_rate=0</em>, <em>fc_dims=None</em>, <em>dropout_p=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/densenet.html#DenseNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.densenet.DenseNet" title="Permalink to this definition"></a></dt>
<dd><p>Densely connected network.</p> <dd><p>Densely connected network.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Huang et al. Densely Connected Convolutional Networks. CVPR 2017.</p> <dt>Reference:</dt>
</dd> <dd>Huang et al. Densely Connected Convolutional Networks. CVPR 2017.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">densenet121</span></code>: DenseNet121.</p></li> <dd><ul class="first last simple">
<li><p><code class="docutils literal notranslate"><span class="pre">densenet169</span></code>: DenseNet169.</p></li> <li><code class="docutils literal notranslate"><span class="pre">densenet121</span></code>: DenseNet121.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">densenet201</span></code>: DenseNet201.</p></li> <li><code class="docutils literal notranslate"><span class="pre">densenet169</span></code>: DenseNet169.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">densenet161</span></code>: DenseNet161.</p></li> <li><code class="docutils literal notranslate"><span class="pre">densenet201</span></code>: DenseNet201.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">densenet121_fc512</span></code>: DenseNet121 + FC.</p></li> <li><code class="docutils literal notranslate"><span class="pre">densenet161</span></code>: DenseNet161.</li>
<li><code class="docutils literal notranslate"><span class="pre">densenet121_fc512</span></code>: DenseNet121 + FC.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -287,14 +297,15 @@ Default is True.</p></li>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.inceptionresnetv2.InceptionResNetV2"> <dt id="torchreid.models.inceptionresnetv2.InceptionResNetV2">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.inceptionresnetv2.</code><code class="sig-name descname">InceptionResNetV2</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss='softmax'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/inceptionresnetv2.html#InceptionResNetV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.inceptionresnetv2.InceptionResNetV2" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.inceptionresnetv2.</code><code class="descname">InceptionResNetV2</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss='softmax'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/inceptionresnetv2.html#InceptionResNetV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.inceptionresnetv2.InceptionResNetV2" title="Permalink to this definition"></a></dt>
<dd><p>Inception-ResNet-V2.</p> <dd><p>Inception-ResNet-V2.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual <dt>Reference:</dt>
Connections on Learning. AAAI 2017.</p> <dd>Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
</dd> Connections on Learning. AAAI 2017.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">inceptionresnetv2</span></code>: Inception-ResNet-V2.</p></li> <dd><ul class="first last simple">
<li><code class="docutils literal notranslate"><span class="pre">inceptionresnetv2</span></code>: Inception-ResNet-V2.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -302,14 +313,15 @@ Connections on Learning. AAAI 2017.</p>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.inceptionv4.InceptionV4"> <dt id="torchreid.models.inceptionv4.InceptionV4">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.inceptionv4.</code><code class="sig-name descname">InceptionV4</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/inceptionv4.html#InceptionV4"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.inceptionv4.InceptionV4" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.inceptionv4.</code><code class="descname">InceptionV4</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/inceptionv4.html#InceptionV4"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.inceptionv4.InceptionV4" title="Permalink to this definition"></a></dt>
<dd><p>Inception-v4.</p> <dd><p>Inception-v4.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual <dt>Reference:</dt>
Connections on Learning. AAAI 2017.</p> <dd>Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
</dd> Connections on Learning. AAAI 2017.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">inceptionv4</span></code>: InceptionV4.</p></li> <dd><ul class="first last simple">
<li><code class="docutils literal notranslate"><span class="pre">inceptionv4</span></code>: InceptionV4.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -317,14 +329,15 @@ Connections on Learning. AAAI 2017.</p>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.xception.Xception"> <dt id="torchreid.models.xception.Xception">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.xception.</code><code class="sig-name descname">Xception</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss</em>, <em class="sig-param">fc_dims=None</em>, <em class="sig-param">dropout_p=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/xception.html#Xception"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.xception.Xception" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.xception.</code><code class="descname">Xception</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>fc_dims=None</em>, <em>dropout_p=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/xception.html#Xception"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.xception.Xception" title="Permalink to this definition"></a></dt>
<dd><p>Xception.</p> <dd><p>Xception.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Chollet. Xception: Deep Learning with Depthwise <dt>Reference:</dt>
Separable Convolutions. CVPR 2017.</p> <dd>Chollet. Xception: Deep Learning with Depthwise
</dd> Separable Convolutions. CVPR 2017.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">xception</span></code>: Xception.</p></li> <dd><ul class="first last simple">
<li><code class="docutils literal notranslate"><span class="pre">xception</span></code>: Xception.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -335,14 +348,15 @@ Separable Convolutions. CVPR 2017.</p>
<h2>Lightweight Models<a class="headerlink" href="#lightweight-models" title="Permalink to this headline"></a></h2> <h2>Lightweight Models<a class="headerlink" href="#lightweight-models" title="Permalink to this headline"></a></h2>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.nasnet.NASNetAMobile"> <dt id="torchreid.models.nasnet.NASNetAMobile">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.nasnet.</code><code class="sig-name descname">NASNetAMobile</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss</em>, <em class="sig-param">stem_filters=32</em>, <em class="sig-param">penultimate_filters=1056</em>, <em class="sig-param">filters_multiplier=2</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/nasnet.html#NASNetAMobile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.nasnet.NASNetAMobile" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.nasnet.</code><code class="descname">NASNetAMobile</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>stem_filters=32</em>, <em>penultimate_filters=1056</em>, <em>filters_multiplier=2</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/nasnet.html#NASNetAMobile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.nasnet.NASNetAMobile" title="Permalink to this definition"></a></dt>
<dd><p>Neural Architecture Search (NAS).</p> <dd><p>Neural Architecture Search (NAS).</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Zoph et al. Learning Transferable Architectures <dt>Reference:</dt>
for Scalable Image Recognition. CVPR 2018.</p> <dd>Zoph et al. Learning Transferable Architectures
</dd> for Scalable Image Recognition. CVPR 2018.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">nasnetamobile</span></code>: NASNet-A Mobile.</p></li> <dd><ul class="first last simple">
<li><code class="docutils literal notranslate"><span class="pre">nasnetamobile</span></code>: NASNet-A Mobile.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -350,15 +364,16 @@ for Scalable Image Recognition. CVPR 2018.</p>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.mobilenetv2.MobileNetV2"> <dt id="torchreid.models.mobilenetv2.MobileNetV2">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.mobilenetv2.</code><code class="sig-name descname">MobileNetV2</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">width_mult=1</em>, <em class="sig-param">loss='softmax'</em>, <em class="sig-param">fc_dims=None</em>, <em class="sig-param">dropout_p=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/mobilenetv2.html#MobileNetV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.mobilenetv2.MobileNetV2" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.mobilenetv2.</code><code class="descname">MobileNetV2</code><span class="sig-paren">(</span><em>num_classes</em>, <em>width_mult=1</em>, <em>loss='softmax'</em>, <em>fc_dims=None</em>, <em>dropout_p=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/mobilenetv2.html#MobileNetV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.mobilenetv2.MobileNetV2" title="Permalink to this definition"></a></dt>
<dd><p>MobileNetV2.</p> <dd><p>MobileNetV2.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Sandler et al. MobileNetV2: Inverted Residuals and <dt>Reference:</dt>
Linear Bottlenecks. CVPR 2018.</p> <dd>Sandler et al. MobileNetV2: Inverted Residuals and
</dd> Linear Bottlenecks. CVPR 2018.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">mobilenetv2_x1_0</span></code>: MobileNetV2 x1.0.</p></li> <dd><ul class="first last simple">
<li><p><code class="docutils literal notranslate"><span class="pre">mobilenetv2_x1_4</span></code>: MobileNetV2 x1.4.</p></li> <li><code class="docutils literal notranslate"><span class="pre">mobilenetv2_x1_0</span></code>: MobileNetV2 x1.0.</li>
<li><code class="docutils literal notranslate"><span class="pre">mobilenetv2_x1_4</span></code>: MobileNetV2 x1.4.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -366,14 +381,15 @@ Linear Bottlenecks. CVPR 2018.</p>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.shufflenet.ShuffleNet"> <dt id="torchreid.models.shufflenet.ShuffleNet">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.shufflenet.</code><code class="sig-name descname">ShuffleNet</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss='softmax'</em>, <em class="sig-param">num_groups=3</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/shufflenet.html#ShuffleNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.shufflenet.ShuffleNet" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.shufflenet.</code><code class="descname">ShuffleNet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss='softmax'</em>, <em>num_groups=3</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/shufflenet.html#ShuffleNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.shufflenet.ShuffleNet" title="Permalink to this definition"></a></dt>
<dd><p>ShuffleNet.</p> <dd><p>ShuffleNet.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Zhang et al. ShuffleNet: An Extremely Efficient Convolutional Neural <dt>Reference:</dt>
Network for Mobile Devices. CVPR 2018.</p> <dd>Zhang et al. ShuffleNet: An Extremely Efficient Convolutional Neural
</dd> Network for Mobile Devices. CVPR 2018.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">shufflenet</span></code>: ShuffleNet (groups=3).</p></li> <dd><ul class="first last simple">
<li><code class="docutils literal notranslate"><span class="pre">shufflenet</span></code>: ShuffleNet (groups=3).</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -381,16 +397,17 @@ Network for Mobile Devices. CVPR 2018.</p>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.squeezenet.SqueezeNet"> <dt id="torchreid.models.squeezenet.SqueezeNet">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.squeezenet.</code><code class="sig-name descname">SqueezeNet</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss</em>, <em class="sig-param">version=1.0</em>, <em class="sig-param">fc_dims=None</em>, <em class="sig-param">dropout_p=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/squeezenet.html#SqueezeNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.squeezenet.SqueezeNet" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.squeezenet.</code><code class="descname">SqueezeNet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>version=1.0</em>, <em>fc_dims=None</em>, <em>dropout_p=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/squeezenet.html#SqueezeNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.squeezenet.SqueezeNet" title="Permalink to this definition"></a></dt>
<dd><p>SqueezeNet.</p> <dd><p>SqueezeNet.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Iandola et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters <dt>Reference:</dt>
and&lt; 0.5 MB model size. arXiv:1602.07360.</p> <dd>Iandola et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
</dd> and&lt; 0.5 MB model size. arXiv:1602.07360.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">squeezenet1_0</span></code>: SqueezeNet (version=1.0).</p></li> <dd><ul class="first last simple">
<li><p><code class="docutils literal notranslate"><span class="pre">squeezenet1_1</span></code>: SqueezeNet (version=1.1).</p></li> <li><code class="docutils literal notranslate"><span class="pre">squeezenet1_0</span></code>: SqueezeNet (version=1.0).</li>
<li><p><code class="docutils literal notranslate"><span class="pre">squeezenet1_0_fc512</span></code>: SqueezeNet (version=1.0) + FC.</p></li> <li><code class="docutils literal notranslate"><span class="pre">squeezenet1_1</span></code>: SqueezeNet (version=1.1).</li>
<li><code class="docutils literal notranslate"><span class="pre">squeezenet1_0_fc512</span></code>: SqueezeNet (version=1.0) + FC.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -398,16 +415,17 @@ and&lt; 0.5 MB model size. arXiv:1602.07360.</p>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.shufflenetv2.ShuffleNetV2"> <dt id="torchreid.models.shufflenetv2.ShuffleNetV2">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.shufflenetv2.</code><code class="sig-name descname">ShuffleNetV2</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss</em>, <em class="sig-param">stages_repeats</em>, <em class="sig-param">stages_out_channels</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/shufflenetv2.html#ShuffleNetV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.shufflenetv2.ShuffleNetV2" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.shufflenetv2.</code><code class="descname">ShuffleNetV2</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>stages_repeats</em>, <em>stages_out_channels</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/shufflenetv2.html#ShuffleNetV2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.shufflenetv2.ShuffleNetV2" title="Permalink to this definition"></a></dt>
<dd><p>ShuffleNetV2.</p> <dd><p>ShuffleNetV2.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.</p> <dt>Reference:</dt>
</dd> <dd>Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x0_5</span></code>: ShuffleNetV2 x0.5.</p></li> <dd><ul class="first last simple">
<li><p><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x1_0</span></code>: ShuffleNetV2 x1.0.</p></li> <li><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x0_5</span></code>: ShuffleNetV2 x0.5.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x1_5</span></code>: ShuffleNetV2 x1.5.</p></li> <li><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x1_0</span></code>: ShuffleNetV2 x1.0.</li>
<li><p><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x2_0</span></code>: ShuffleNetV2 x2.0.</p></li> <li><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x1_5</span></code>: ShuffleNetV2 x1.5.</li>
<li><code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x2_0</span></code>: ShuffleNetV2 x2.0.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -418,14 +436,15 @@ and&lt; 0.5 MB model size. arXiv:1602.07360.</p>
<h2>ReID-specific Models<a class="headerlink" href="#reid-specific-models" title="Permalink to this headline"></a></h2> <h2>ReID-specific Models<a class="headerlink" href="#reid-specific-models" title="Permalink to this headline"></a></h2>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.mudeep.MuDeep"> <dt id="torchreid.models.mudeep.MuDeep">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.mudeep.</code><code class="sig-name descname">MuDeep</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss='softmax'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/mudeep.html#MuDeep"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.mudeep.MuDeep" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.mudeep.</code><code class="descname">MuDeep</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss='softmax'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/mudeep.html#MuDeep"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.mudeep.MuDeep" title="Permalink to this definition"></a></dt>
<dd><p>Multiscale deep neural network.</p> <dd><p>Multiscale deep neural network.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Qian et al. Multi-scale Deep Learning Architectures <dt>Reference:</dt>
for Person Re-identification. ICCV 2017.</p> <dd>Qian et al. Multi-scale Deep Learning Architectures
</dd> for Person Re-identification. ICCV 2017.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">mudeep</span></code>: Multiscale deep neural network.</p></li> <dd><ul class="first last simple">
<li><code class="docutils literal notranslate"><span class="pre">mudeep</span></code>: Multiscale deep neural network.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -433,14 +452,15 @@ for Person Re-identification. ICCV 2017.</p>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.resnetmid.ResNetMid"> <dt id="torchreid.models.resnetmid.ResNetMid">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.resnetmid.</code><code class="sig-name descname">ResNetMid</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss</em>, <em class="sig-param">block</em>, <em class="sig-param">layers</em>, <em class="sig-param">last_stride=2</em>, <em class="sig-param">fc_dims=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/resnetmid.html#ResNetMid"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.resnetmid.ResNetMid" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.resnetmid.</code><code class="descname">ResNetMid</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>block</em>, <em>layers</em>, <em>last_stride=2</em>, <em>fc_dims=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/resnetmid.html#ResNetMid"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.resnetmid.ResNetMid" title="Permalink to this definition"></a></dt>
<dd><p>Residual network + mid-level features.</p> <dd><p>Residual network + mid-level features.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Yu et al. The Devil is in the Middle: Exploiting Mid-level Representations for <dt>Reference:</dt>
Cross-Domain Instance Matching. arXiv:1711.08106.</p> <dd>Yu et al. The Devil is in the Middle: Exploiting Mid-level Representations for
</dd> Cross-Domain Instance Matching. arXiv:1711.08106.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">resnet50mid</span></code>: ResNet50 + mid-level feature fusion.</p></li> <dd><ul class="first last simple">
<li><code class="docutils literal notranslate"><span class="pre">resnet50mid</span></code>: ResNet50 + mid-level feature fusion.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -448,13 +468,14 @@ Cross-Domain Instance Matching. arXiv:1711.08106.</p>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.hacnn.HACNN"> <dt id="torchreid.models.hacnn.HACNN">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.hacnn.</code><code class="sig-name descname">HACNN</code><span class="sig-paren">(</span><em class="sig-param">num_classes, loss='softmax', nchannels=[128, 256, 384], feat_dim=512, learn_region=True, use_gpu=True, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/hacnn.html#HACNN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.hacnn.HACNN" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.hacnn.</code><code class="descname">HACNN</code><span class="sig-paren">(</span><em>num_classes, loss='softmax', nchannels=[128, 256, 384], feat_dim=512, learn_region=True, use_gpu=True, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/hacnn.html#HACNN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.hacnn.HACNN" title="Permalink to this definition"></a></dt>
<dd><p>Harmonious Attention Convolutional Neural Network.</p> <dd><p>Harmonious Attention Convolutional Neural Network.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Li et al. Harmonious Attention Network for Person Re-identification. CVPR 2018.</p> <dt>Reference:</dt>
</dd> <dd>Li et al. Harmonious Attention Network for Person Re-identification. CVPR 2018.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">hacnn</span></code>: HACNN.</p></li> <dd><ul class="first last simple">
<li><code class="docutils literal notranslate"><span class="pre">hacnn</span></code>: HACNN.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -462,15 +483,16 @@ Cross-Domain Instance Matching. arXiv:1711.08106.</p>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.pcb.PCB"> <dt id="torchreid.models.pcb.PCB">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.pcb.</code><code class="sig-name descname">PCB</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">loss</em>, <em class="sig-param">block</em>, <em class="sig-param">layers</em>, <em class="sig-param">parts=6</em>, <em class="sig-param">reduced_dim=256</em>, <em class="sig-param">nonlinear='relu'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/pcb.html#PCB"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.pcb.PCB" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.pcb.</code><code class="descname">PCB</code><span class="sig-paren">(</span><em>num_classes</em>, <em>loss</em>, <em>block</em>, <em>layers</em>, <em>parts=6</em>, <em>reduced_dim=256</em>, <em>nonlinear='relu'</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/pcb.html#PCB"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.pcb.PCB" title="Permalink to this definition"></a></dt>
<dd><p>Part-based Convolutional Baseline.</p> <dd><p>Part-based Convolutional Baseline.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Sun et al. Beyond Part Models: Person Retrieval with Refined <dt>Reference:</dt>
Part Pooling (and A Strong Convolutional Baseline). ECCV 2018.</p> <dd>Sun et al. Beyond Part Models: Person Retrieval with Refined
</dd> Part Pooling (and A Strong Convolutional Baseline). ECCV 2018.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">pcb_p4</span></code>: PCB with 4-part strips.</p></li> <dd><ul class="first last simple">
<li><p><code class="docutils literal notranslate"><span class="pre">pcb_p6</span></code>: PCB with 6-part strips.</p></li> <li><code class="docutils literal notranslate"><span class="pre">pcb_p4</span></code>: PCB with 4-part strips.</li>
<li><code class="docutils literal notranslate"><span class="pre">pcb_p6</span></code>: PCB with 6-part strips.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -478,14 +500,15 @@ Part Pooling (and A Strong Convolutional Baseline). ECCV 2018.</p>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.mlfn.MLFN"> <dt id="torchreid.models.mlfn.MLFN">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.mlfn.</code><code class="sig-name descname">MLFN</code><span class="sig-paren">(</span><em class="sig-param">num_classes, loss='softmax', groups=32, channels=[64, 256, 512, 1024, 2048], embed_dim=1024, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/mlfn.html#MLFN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.mlfn.MLFN" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.mlfn.</code><code class="descname">MLFN</code><span class="sig-paren">(</span><em>num_classes, loss='softmax', groups=32, channels=[64, 256, 512, 1024, 2048], embed_dim=1024, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/mlfn.html#MLFN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.mlfn.MLFN" title="Permalink to this definition"></a></dt>
<dd><p>Multi-Level Factorisation Net.</p> <dd><p>Multi-Level Factorisation Net.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><p>Chang et al. Multi-Level Factorisation Net for <dt>Reference:</dt>
Person Re-Identification. CVPR 2018.</p> <dd>Chang et al. Multi-Level Factorisation Net for
</dd> Person Re-Identification. CVPR 2018.</dd>
<dt>Public keys:</dt><dd><ul class="simple"> <dt>Public keys:</dt>
<li><p><code class="docutils literal notranslate"><span class="pre">mlfn</span></code>: MLFN (Multi-Level Factorisation Net).</p></li> <dd><ul class="first last simple">
<li><code class="docutils literal notranslate"><span class="pre">mlfn</span></code>: MLFN (Multi-Level Factorisation Net).</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -493,13 +516,14 @@ Person Re-Identification. CVPR 2018.</p>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.osnet.OSNet"> <dt id="torchreid.models.osnet.OSNet">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.osnet.</code><code class="sig-name descname">OSNet</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">blocks</em>, <em class="sig-param">layers</em>, <em class="sig-param">channels</em>, <em class="sig-param">feature_dim=512</em>, <em class="sig-param">loss='softmax'</em>, <em class="sig-param">IN=False</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/osnet.html#OSNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.osnet.OSNet" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.osnet.</code><code class="descname">OSNet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>blocks</em>, <em>layers</em>, <em>channels</em>, <em>feature_dim=512</em>, <em>loss='softmax'</em>, <em>IN=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/osnet.html#OSNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.osnet.OSNet" title="Permalink to this definition"></a></dt>
<dd><p>Omni-Scale Network.</p> <dd><p>Omni-Scale Network.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><ul class="simple"> <dt>Reference:</dt>
<li><p>Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.</p></li> <dd><ul class="first last simple">
<li><p>Zhou et al. Learning Generalisable Omni-Scale Representations <li>Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.</li>
for Person Re-Identification. arXiv preprint, 2019.</p></li> <li>Zhou et al. Learning Generalisable Omni-Scale Representations
for Person Re-Identification. arXiv preprint, 2019.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
@ -507,13 +531,14 @@ for Person Re-Identification. arXiv preprint, 2019.</p></li>
<dl class="class"> <dl class="class">
<dt id="torchreid.models.osnet_ain.OSNet"> <dt id="torchreid.models.osnet_ain.OSNet">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.models.osnet_ain.</code><code class="sig-name descname">OSNet</code><span class="sig-paren">(</span><em class="sig-param">num_classes</em>, <em class="sig-param">blocks</em>, <em class="sig-param">layers</em>, <em class="sig-param">channels</em>, <em class="sig-param">feature_dim=512</em>, <em class="sig-param">loss='softmax'</em>, <em class="sig-param">conv1_IN=False</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/osnet_ain.html#OSNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.osnet_ain.OSNet" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.models.osnet_ain.</code><code class="descname">OSNet</code><span class="sig-paren">(</span><em>num_classes</em>, <em>blocks</em>, <em>layers</em>, <em>channels</em>, <em>feature_dim=512</em>, <em>loss='softmax'</em>, <em>conv1_IN=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/models/osnet_ain.html#OSNet"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.models.osnet_ain.OSNet" title="Permalink to this definition"></a></dt>
<dd><p>Omni-Scale Network.</p> <dd><p>Omni-Scale Network.</p>
<dl class="simple"> <dl class="docutils">
<dt>Reference:</dt><dd><ul class="simple"> <dt>Reference:</dt>
<li><p>Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.</p></li> <dd><ul class="first last simple">
<li><p>Zhou et al. Learning Generalisable Omni-Scale Representations <li>Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.</li>
for Person Re-Identification. arXiv preprint, 2019.</p></li> <li>Zhou et al. Learning Generalisable Omni-Scale Representations
for Person Re-Identification. arXiv preprint, 2019.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>

82
pkg/optim.html
View File

@ -178,33 +178,38 @@
<span id="optimizer"></span><h2>Optimizer<a class="headerlink" href="#module-torchreid.optim.optimizer" title="Permalink to this headline"></a></h2> <span id="optimizer"></span><h2>Optimizer<a class="headerlink" href="#module-torchreid.optim.optimizer" title="Permalink to this headline"></a></h2>
<dl class="function"> <dl class="function">
<dt id="torchreid.optim.optimizer.build_optimizer"> <dt id="torchreid.optim.optimizer.build_optimizer">
<code class="sig-prename descclassname">torchreid.optim.optimizer.</code><code class="sig-name descname">build_optimizer</code><span class="sig-paren">(</span><em class="sig-param">model</em>, <em class="sig-param">optim='adam'</em>, <em class="sig-param">lr=0.0003</em>, <em class="sig-param">weight_decay=0.0005</em>, <em class="sig-param">momentum=0.9</em>, <em class="sig-param">sgd_dampening=0</em>, <em class="sig-param">sgd_nesterov=False</em>, <em class="sig-param">rmsprop_alpha=0.99</em>, <em class="sig-param">adam_beta1=0.9</em>, <em class="sig-param">adam_beta2=0.99</em>, <em class="sig-param">staged_lr=False</em>, <em class="sig-param">new_layers=''</em>, <em class="sig-param">base_lr_mult=0.1</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/optim/optimizer.html#build_optimizer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.optim.optimizer.build_optimizer" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.optim.optimizer.</code><code class="descname">build_optimizer</code><span class="sig-paren">(</span><em>model</em>, <em>optim='adam'</em>, <em>lr=0.0003</em>, <em>weight_decay=0.0005</em>, <em>momentum=0.9</em>, <em>sgd_dampening=0</em>, <em>sgd_nesterov=False</em>, <em>rmsprop_alpha=0.99</em>, <em>adam_beta1=0.9</em>, <em>adam_beta2=0.99</em>, <em>staged_lr=False</em>, <em>new_layers=''</em>, <em>base_lr_mult=0.1</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/optim/optimizer.html#build_optimizer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.optim.optimizer.build_optimizer" title="Permalink to this definition"></a></dt>
<dd><p>A function wrapper for building an optimizer.</p> <dd><p>A function wrapper for building an optimizer.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>model</strong> (<em>nn.Module</em>) – model.</p></li> <tbody valign="top">
<li><p><strong>optim</strong> (<em>str</em><em>, </em><em>optional</em>) – optimizer. Default is “adam”.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>lr</strong> (<em>float</em><em>, </em><em>optional</em>) – learning rate. Default is 0.0003.</p></li> <li><strong>model</strong> (<em>nn.Module</em>) – model.</li>
<li><p><strong>weight_decay</strong> (<em>float</em><em>, </em><em>optional</em>) – weight decay (L2 penalty). Default is 5e-04.</p></li> <li><strong>optim</strong> (<em>str</em><em>, </em><em>optional</em>) – optimizer. Default is “adam”.</li>
<li><p><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em>) – momentum factor in sgd. Default is 0.9,</p></li> <li><strong>lr</strong> (<em>float</em><em>, </em><em>optional</em>) – learning rate. Default is 0.0003.</li>
<li><p><strong>sgd_dampening</strong> (<em>float</em><em>, </em><em>optional</em>) – dampening for momentum. Default is 0.</p></li> <li><strong>weight_decay</strong> (<em>float</em><em>, </em><em>optional</em>) – weight decay (L2 penalty). Default is 5e-04.</li>
<li><p><strong>sgd_nesterov</strong> (<em>bool</em><em>, </em><em>optional</em>) – enables Nesterov momentum. Default is False.</p></li> <li><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em>) – momentum factor in sgd. Default is 0.9,</li>
<li><p><strong>rmsprop_alpha</strong> (<em>float</em><em>, </em><em>optional</em>) – smoothing constant for rmsprop. Default is 0.99.</p></li> <li><strong>sgd_dampening</strong> (<em>float</em><em>, </em><em>optional</em>) – dampening for momentum. Default is 0.</li>
<li><p><strong>adam_beta1</strong> (<em>float</em><em>, </em><em>optional</em>) – beta-1 value in adam. Default is 0.9.</p></li> <li><strong>sgd_nesterov</strong> (<em>bool</em><em>, </em><em>optional</em>) – enables Nesterov momentum. Default is False.</li>
<li><p><strong>adam_beta2</strong> (<em>float</em><em>, </em><em>optional</em>) – beta-2 value in adam. Default is 0.99,</p></li> <li><strong>rmsprop_alpha</strong> (<em>float</em><em>, </em><em>optional</em>) – smoothing constant for rmsprop. Default is 0.99.</li>
<li><p><strong>staged_lr</strong> (<em>bool</em><em>, </em><em>optional</em>) – uses different learning rates for base and new layers. Base <li><strong>adam_beta1</strong> (<em>float</em><em>, </em><em>optional</em>) – beta-1 value in adam. Default is 0.9.</li>
<li><strong>adam_beta2</strong> (<em>float</em><em>, </em><em>optional</em>) – beta-2 value in adam. Default is 0.99,</li>
<li><strong>staged_lr</strong> (<em>bool</em><em>, </em><em>optional</em>) – uses different learning rates for base and new layers. Base
layers are pretrained layers while new layers are randomly initialized, e.g. the layers are pretrained layers while new layers are randomly initialized, e.g. the
identity classification layer. Enabling <code class="docutils literal notranslate"><span class="pre">staged_lr</span></code> can allow the base layers to identity classification layer. Enabling <code class="docutils literal notranslate"><span class="pre">staged_lr</span></code> can allow the base layers to
be trained with a smaller learning rate determined by <code class="docutils literal notranslate"><span class="pre">base_lr_mult</span></code>, while the new be trained with a smaller learning rate determined by <code class="docutils literal notranslate"><span class="pre">base_lr_mult</span></code>, while the new
layers will take the <code class="docutils literal notranslate"><span class="pre">lr</span></code>. Default is False.</p></li> layers will take the <code class="docutils literal notranslate"><span class="pre">lr</span></code>. Default is False.</li>
<li><p><strong>new_layers</strong> (<em>str</em><em> or </em><em>list</em>) – attribute names in <code class="docutils literal notranslate"><span class="pre">model</span></code>. Default is empty.</p></li> <li><strong>new_layers</strong> (<em>str</em><em> or </em><em>list</em>) – attribute names in <code class="docutils literal notranslate"><span class="pre">model</span></code>. Default is empty.</li>
<li><p><strong>base_lr_mult</strong> (<em>float</em><em>, </em><em>optional</em>) – learning rate multiplier for base layers. Default is 0.1.</p></li> <li><strong>base_lr_mult</strong> (<em>float</em><em>, </em><em>optional</em>) – learning rate multiplier for base layers. Default is 0.1.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># A normal optimizer can be built by</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># A normal optimizer can be built by</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_optimizer</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="s1">&#39;sgd&#39;</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_optimizer</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="s1">&#39;sgd&#39;</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># If you want to use a smaller learning rate for pretrained layers</span> <span class="gp">&gt;&gt;&gt; </span><span class="c1"># If you want to use a smaller learning rate for pretrained layers</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># and the attribute name for the randomly initialized layer is &#39;classifier&#39;,</span> <span class="gp">&gt;&gt;&gt; </span><span class="c1"># and the attribute name for the randomly initialized layer is &#39;classifier&#39;,</span>
@ -232,23 +237,28 @@ layers will take the <code class="docutils literal notranslate"><span class="pre
<span id="lr-scheduler"></span><h2>LR Scheduler<a class="headerlink" href="#module-torchreid.optim.lr_scheduler" title="Permalink to this headline"></a></h2> <span id="lr-scheduler"></span><h2>LR Scheduler<a class="headerlink" href="#module-torchreid.optim.lr_scheduler" title="Permalink to this headline"></a></h2>
<dl class="function"> <dl class="function">
<dt id="torchreid.optim.lr_scheduler.build_lr_scheduler"> <dt id="torchreid.optim.lr_scheduler.build_lr_scheduler">
<code class="sig-prename descclassname">torchreid.optim.lr_scheduler.</code><code class="sig-name descname">build_lr_scheduler</code><span class="sig-paren">(</span><em class="sig-param">optimizer</em>, <em class="sig-param">lr_scheduler='single_step'</em>, <em class="sig-param">stepsize=1</em>, <em class="sig-param">gamma=0.1</em>, <em class="sig-param">max_epoch=1</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/optim/lr_scheduler.html#build_lr_scheduler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.optim.lr_scheduler.build_lr_scheduler" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.optim.lr_scheduler.</code><code class="descname">build_lr_scheduler</code><span class="sig-paren">(</span><em>optimizer</em>, <em>lr_scheduler='single_step'</em>, <em>stepsize=1</em>, <em>gamma=0.1</em>, <em>max_epoch=1</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/optim/lr_scheduler.html#build_lr_scheduler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.optim.lr_scheduler.build_lr_scheduler" title="Permalink to this definition"></a></dt>
<dd><p>A function wrapper for building a learning rate scheduler.</p> <dd><p>A function wrapper for building a learning rate scheduler.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</p></li> <tbody valign="top">
<li><p><strong>lr_scheduler</strong> (<em>str</em><em>, </em><em>optional</em>) – learning rate scheduler method. Default is single_step.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>stepsize</strong> (<em>int</em><em> or </em><em>list</em><em>, </em><em>optional</em>) – step size to decay learning rate. When <code class="docutils literal notranslate"><span class="pre">lr_scheduler</span></code> <li><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</li>
<li><strong>lr_scheduler</strong> (<em>str</em><em>, </em><em>optional</em>) – learning rate scheduler method. Default is single_step.</li>
<li><strong>stepsize</strong> (<em>int</em><em> or </em><em>list</em><em>, </em><em>optional</em>) – step size to decay learning rate. When <code class="docutils literal notranslate"><span class="pre">lr_scheduler</span></code>
is “single_step”, <code class="docutils literal notranslate"><span class="pre">stepsize</span></code> should be an integer. When <code class="docutils literal notranslate"><span class="pre">lr_scheduler</span></code> is is “single_step”, <code class="docutils literal notranslate"><span class="pre">stepsize</span></code> should be an integer. When <code class="docutils literal notranslate"><span class="pre">lr_scheduler</span></code> is
“multi_step”, <code class="docutils literal notranslate"><span class="pre">stepsize</span></code> is a list. Default is 1.</p></li> “multi_step”, <code class="docutils literal notranslate"><span class="pre">stepsize</span></code> is a list. Default is 1.</li>
<li><p><strong>gamma</strong> (<em>float</em><em>, </em><em>optional</em>) – decay rate. Default is 0.1.</p></li> <li><strong>gamma</strong> (<em>float</em><em>, </em><em>optional</em>) – decay rate. Default is 0.1.</li>
<li><p><strong>max_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum epoch (for cosine annealing). Default is 1.</p></li> <li><strong>max_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum epoch (for cosine annealing). Default is 1.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># Decay learning rate by every 20 epochs.</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># Decay learning rate by every 20 epochs.</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">scheduler</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_lr_scheduler</span><span class="p">(</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">scheduler</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_lr_scheduler</span><span class="p">(</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">lr_scheduler</span><span class="o">=</span><span class="s1">&#39;single_step&#39;</span><span class="p">,</span> <span class="n">stepsize</span><span class="o">=</span><span class="mi">20</span> <span class="gp">&gt;&gt;&gt; </span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">lr_scheduler</span><span class="o">=</span><span class="s1">&#39;single_step&#39;</span><span class="p">,</span> <span class="n">stepsize</span><span class="o">=</span><span class="mi">20</span>
<span class="gp">&gt;&gt;&gt; </span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="p">)</span>

402
pkg/utils.html
View File

@ -181,10 +181,11 @@
<span id="average-meter"></span><h2>Average Meter<a class="headerlink" href="#module-torchreid.utils.avgmeter" title="Permalink to this headline"></a></h2> <span id="average-meter"></span><h2>Average Meter<a class="headerlink" href="#module-torchreid.utils.avgmeter" title="Permalink to this headline"></a></h2>
<dl class="class"> <dl class="class">
<dt id="torchreid.utils.avgmeter.AverageMeter"> <dt id="torchreid.utils.avgmeter.AverageMeter">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.utils.avgmeter.</code><code class="sig-name descname">AverageMeter</code><a class="reference internal" href="../_modules/torchreid/utils/avgmeter.html#AverageMeter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.avgmeter.AverageMeter" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.utils.avgmeter.</code><code class="descname">AverageMeter</code><a class="reference internal" href="../_modules/torchreid/utils/avgmeter.html#AverageMeter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.avgmeter.AverageMeter" title="Permalink to this definition"></a></dt>
<dd><p>Computes and stores the average and current value.</p> <dd><p>Computes and stores the average and current value.</p>
<dl> <dl class="docutils">
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># Initialize a meter to record loss</span> <dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># Initialize a meter to record loss</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">losses</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">()</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">losses</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Update meter after every minibatch update</span> <span class="gp">&gt;&gt;&gt; </span><span class="c1"># Update meter after every minibatch update</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">losses</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">loss_value</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">losses</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">loss_value</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
@ -199,16 +200,20 @@
<span id="loggers"></span><h2>Loggers<a class="headerlink" href="#module-torchreid.utils.loggers" title="Permalink to this headline"></a></h2> <span id="loggers"></span><h2>Loggers<a class="headerlink" href="#module-torchreid.utils.loggers" title="Permalink to this headline"></a></h2>
<dl class="class"> <dl class="class">
<dt id="torchreid.utils.loggers.Logger"> <dt id="torchreid.utils.loggers.Logger">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.utils.loggers.</code><code class="sig-name descname">Logger</code><span class="sig-paren">(</span><em class="sig-param">fpath=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/loggers.html#Logger"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.loggers.Logger" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.utils.loggers.</code><code class="descname">Logger</code><span class="sig-paren">(</span><em>fpath=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/loggers.html#Logger"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.loggers.Logger" title="Permalink to this definition"></a></dt>
<dd><p>Writes console output to external text file.</p> <dd><p>Writes console output to external text file.</p>
<p>Imported from <a class="reference external" href="https://github.com/Cysu/open-reid/blob/master/reid/utils/logging.py">https://github.com/Cysu/open-reid/blob/master/reid/utils/logging.py</a></p> <p>Imported from <a class="reference external" href="https://github.com/Cysu/open-reid/blob/master/reid/utils/logging.py">https://github.com/Cysu/open-reid/blob/master/reid/utils/logging.py</a></p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><p><strong>fpath</strong> (<em>str</em>) – directory to save logging file.</p> <col class="field-body" />
</dd> <tbody valign="top">
</dl> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fpath</strong> (<em>str</em>) – directory to save logging file.</td>
<dl> </tr>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">sys</span> </tbody>
</table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">sys</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">os</span> <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">os</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">os.path</span> <span class="k">as</span> <span class="nn">osp</span> <span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">os.path</span> <span class="k">as</span> <span class="nn">osp</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">Logger</span> <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">Logger</span>
@ -223,20 +228,25 @@
<dl class="class"> <dl class="class">
<dt id="torchreid.utils.loggers.RankLogger"> <dt id="torchreid.utils.loggers.RankLogger">
<em class="property">class </em><code class="sig-prename descclassname">torchreid.utils.loggers.</code><code class="sig-name descname">RankLogger</code><span class="sig-paren">(</span><em class="sig-param">sources</em>, <em class="sig-param">targets</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/loggers.html#RankLogger"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.loggers.RankLogger" title="Permalink to this definition"></a></dt> <em class="property">class </em><code class="descclassname">torchreid.utils.loggers.</code><code class="descname">RankLogger</code><span class="sig-paren">(</span><em>sources</em>, <em>targets</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/loggers.html#RankLogger"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.loggers.RankLogger" title="Permalink to this definition"></a></dt>
<dd><p>Records the rank1 matching accuracy obtained for each <dd><p>Records the rank1 matching accuracy obtained for each
test dataset at specified evaluation steps and provides a function test dataset at specified evaluation steps and provides a function
to show the summarized results, which are convenient for analysis.</p> to show the summarized results, which are convenient for analysis.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>sources</strong> (<em>str</em><em> or </em><em>list</em>) – source dataset name(s).</p></li> <tbody valign="top">
<li><p><strong>targets</strong> (<em>str</em><em> or </em><em>list</em>) – target dataset name(s).</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>sources</strong> (<em>str</em><em> or </em><em>list</em>) – source dataset name(s).</li>
<li><strong>targets</strong> (<em>str</em><em> or </em><em>list</em>) – target dataset name(s).</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">RankLogger</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">RankLogger</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;market1501&#39;</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;market1501&#39;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">t</span> <span class="o">=</span> <span class="s1">&#39;market1501&#39;</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">t</span> <span class="o">=</span> <span class="s1">&#39;market1501&#39;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">ranklogger</span> <span class="o">=</span> <span class="n">RankLogger</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">ranklogger</span> <span class="o">=</span> <span class="n">RankLogger</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
@ -276,23 +286,27 @@ to show the summarized results, which are convenient for analysis.</p>
</dl> </dl>
<dl class="method"> <dl class="method">
<dt id="torchreid.utils.loggers.RankLogger.show_summary"> <dt id="torchreid.utils.loggers.RankLogger.show_summary">
<code class="sig-name descname">show_summary</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/loggers.html#RankLogger.show_summary"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.loggers.RankLogger.show_summary" title="Permalink to this definition"></a></dt> <code class="descname">show_summary</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/loggers.html#RankLogger.show_summary"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.loggers.RankLogger.show_summary" title="Permalink to this definition"></a></dt>
<dd><p>Shows saved results.</p> <dd><p>Shows saved results.</p>
</dd></dl> </dd></dl>
<dl class="method"> <dl class="method">
<dt id="torchreid.utils.loggers.RankLogger.write"> <dt id="torchreid.utils.loggers.RankLogger.write">
<code class="sig-name descname">write</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">epoch</em>, <em class="sig-param">rank1</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/loggers.html#RankLogger.write"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.loggers.RankLogger.write" title="Permalink to this definition"></a></dt> <code class="descname">write</code><span class="sig-paren">(</span><em>name</em>, <em>epoch</em>, <em>rank1</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/loggers.html#RankLogger.write"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.loggers.RankLogger.write" title="Permalink to this definition"></a></dt>
<dd><p>Writes result.</p> <dd><p>Writes result.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>name</strong> (<em>str</em>) – dataset name.</p></li> <tbody valign="top">
<li><p><strong>epoch</strong> (<em>int</em>) – current epoch.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>rank1</strong> (<em>float</em>) – rank1 result.</p></li> <li><strong>name</strong> (<em>str</em>) – dataset name.</li>
<li><strong>epoch</strong> (<em>int</em>) – current epoch.</li>
<li><strong>rank1</strong> (<em>float</em>) – rank1 result.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
</dd></dl> </dd></dl>
</dd></dl> </dd></dl>
@ -302,67 +316,75 @@ to show the summarized results, which are convenient for analysis.</p>
<span id="generic-tools"></span><h2>Generic Tools<a class="headerlink" href="#module-torchreid.utils.tools" title="Permalink to this headline"></a></h2> <span id="generic-tools"></span><h2>Generic Tools<a class="headerlink" href="#module-torchreid.utils.tools" title="Permalink to this headline"></a></h2>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.tools.mkdir_if_missing"> <dt id="torchreid.utils.tools.mkdir_if_missing">
<code class="sig-prename descclassname">torchreid.utils.tools.</code><code class="sig-name descname">mkdir_if_missing</code><span class="sig-paren">(</span><em class="sig-param">dirname</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#mkdir_if_missing"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.mkdir_if_missing" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.tools.</code><code class="descname">mkdir_if_missing</code><span class="sig-paren">(</span><em>dirname</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#mkdir_if_missing"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.mkdir_if_missing" title="Permalink to this definition"></a></dt>
<dd><p>Creates dirname if it is missing.</p> <dd><p>Creates dirname if it is missing.</p>
</dd></dl> </dd></dl>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.tools.check_isfile"> <dt id="torchreid.utils.tools.check_isfile">
<code class="sig-prename descclassname">torchreid.utils.tools.</code><code class="sig-name descname">check_isfile</code><span class="sig-paren">(</span><em class="sig-param">fpath</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#check_isfile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.check_isfile" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.tools.</code><code class="descname">check_isfile</code><span class="sig-paren">(</span><em>fpath</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#check_isfile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.check_isfile" title="Permalink to this definition"></a></dt>
<dd><p>Checks if the given path is a file.</p> <dd><p>Checks if the given path is a file.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><p><strong>fpath</strong> (<em>str</em>) – file path.</p> <col class="field-body" />
</dd> <tbody valign="top">
<dt class="field-even">Returns</dt> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fpath</strong> (<em>str</em>) – file path.</td>
<dd class="field-even"><p>bool</p> </tr>
</dd> <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">bool</td>
</dl> </tr>
</tbody>
</table>
</dd></dl> </dd></dl>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.tools.read_json"> <dt id="torchreid.utils.tools.read_json">
<code class="sig-prename descclassname">torchreid.utils.tools.</code><code class="sig-name descname">read_json</code><span class="sig-paren">(</span><em class="sig-param">fpath</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#read_json"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.read_json" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.tools.</code><code class="descname">read_json</code><span class="sig-paren">(</span><em>fpath</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#read_json"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.read_json" title="Permalink to this definition"></a></dt>
<dd><p>Reads json file from a path.</p> <dd><p>Reads json file from a path.</p>
</dd></dl> </dd></dl>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.tools.write_json"> <dt id="torchreid.utils.tools.write_json">
<code class="sig-prename descclassname">torchreid.utils.tools.</code><code class="sig-name descname">write_json</code><span class="sig-paren">(</span><em class="sig-param">obj</em>, <em class="sig-param">fpath</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#write_json"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.write_json" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.tools.</code><code class="descname">write_json</code><span class="sig-paren">(</span><em>obj</em>, <em>fpath</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#write_json"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.write_json" title="Permalink to this definition"></a></dt>
<dd><p>Writes to a json file.</p> <dd><p>Writes to a json file.</p>
</dd></dl> </dd></dl>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.tools.download_url"> <dt id="torchreid.utils.tools.download_url">
<code class="sig-prename descclassname">torchreid.utils.tools.</code><code class="sig-name descname">download_url</code><span class="sig-paren">(</span><em class="sig-param">url</em>, <em class="sig-param">dst</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#download_url"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.download_url" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.tools.</code><code class="descname">download_url</code><span class="sig-paren">(</span><em>url</em>, <em>dst</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#download_url"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.download_url" title="Permalink to this definition"></a></dt>
<dd><p>Downloads file from a url to a destination.</p> <dd><p>Downloads file from a url to a destination.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>url</strong> (<em>str</em>) – url to download file.</p></li> <tbody valign="top">
<li><p><strong>dst</strong> (<em>str</em>) – destination path.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>url</strong> (<em>str</em>) – url to download file.</li>
<li><strong>dst</strong> (<em>str</em>) – destination path.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
</dd></dl> </dd></dl>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.tools.read_image"> <dt id="torchreid.utils.tools.read_image">
<code class="sig-prename descclassname">torchreid.utils.tools.</code><code class="sig-name descname">read_image</code><span class="sig-paren">(</span><em class="sig-param">path</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#read_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.read_image" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.tools.</code><code class="descname">read_image</code><span class="sig-paren">(</span><em>path</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#read_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.read_image" title="Permalink to this definition"></a></dt>
<dd><p>Reads image from path using <code class="docutils literal notranslate"><span class="pre">PIL.Image</span></code>.</p> <dd><p>Reads image from path using <code class="docutils literal notranslate"><span class="pre">PIL.Image</span></code>.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><p><strong>path</strong> (<em>str</em>) – path to an image.</p> <col class="field-body" />
</dd> <tbody valign="top">
<dt class="field-even">Returns</dt> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>path</strong> (<em>str</em>) – path to an image.</td>
<dd class="field-even"><p>PIL image</p> </tr>
</dd> <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">PIL image</td>
</dl> </tr>
</tbody>
</table>
</dd></dl> </dd></dl>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.tools.collect_env_info"> <dt id="torchreid.utils.tools.collect_env_info">
<code class="sig-prename descclassname">torchreid.utils.tools.</code><code class="sig-name descname">collect_env_info</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#collect_env_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.collect_env_info" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.tools.</code><code class="descname">collect_env_info</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/tools.html#collect_env_info"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.tools.collect_env_info" title="Permalink to this definition"></a></dt>
<dd><p>Returns env info as a string.</p> <dd><p>Returns env info as a string.</p>
<p>Code source: github.com/facebookresearch/maskrcnn-benchmark</p> <p>Code source: github.com/facebookresearch/maskrcnn-benchmark</p>
</dd></dl> </dd></dl>
@ -372,26 +394,30 @@ to show the summarized results, which are convenient for analysis.</p>
<span id="reid-tools"></span><h2>ReID Tools<a class="headerlink" href="#module-torchreid.utils.reidtools" title="Permalink to this headline"></a></h2> <span id="reid-tools"></span><h2>ReID Tools<a class="headerlink" href="#module-torchreid.utils.reidtools" title="Permalink to this headline"></a></h2>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.reidtools.visualize_ranked_results"> <dt id="torchreid.utils.reidtools.visualize_ranked_results">
<code class="sig-prename descclassname">torchreid.utils.reidtools.</code><code class="sig-name descname">visualize_ranked_results</code><span class="sig-paren">(</span><em class="sig-param">distmat</em>, <em class="sig-param">dataset</em>, <em class="sig-param">data_type</em>, <em class="sig-param">width=128</em>, <em class="sig-param">height=256</em>, <em class="sig-param">save_dir=''</em>, <em class="sig-param">topk=10</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/reidtools.html#visualize_ranked_results"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.reidtools.visualize_ranked_results" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.reidtools.</code><code class="descname">visualize_ranked_results</code><span class="sig-paren">(</span><em>distmat</em>, <em>dataset</em>, <em>data_type</em>, <em>width=128</em>, <em>height=256</em>, <em>save_dir=''</em>, <em>topk=10</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/reidtools.html#visualize_ranked_results"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.reidtools.visualize_ranked_results" title="Permalink to this definition"></a></dt>
<dd><p>Visualizes ranked results.</p> <dd><p>Visualizes ranked results.</p>
<p>Supports both image-reid and video-reid.</p> <p>Supports both image-reid and video-reid.</p>
<p>For image-reid, ranks will be plotted in a single figure. For video-reid, ranks will be <p>For image-reid, ranks will be plotted in a single figure. For video-reid, ranks will be
saved in folders each containing a tracklet.</p> saved in folders each containing a tracklet.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>distmat</strong> (<em>numpy.ndarray</em>) – distance matrix of shape (num_query, num_gallery).</p></li> <tbody valign="top">
<li><p><strong>dataset</strong> (<em>tuple</em>) – a 2-tuple containing (query, gallery), each of which contains <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
tuples of (img_path(s), pid, camid).</p></li> <li><strong>distmat</strong> (<em>numpy.ndarray</em>) – distance matrix of shape (num_query, num_gallery).</li>
<li><p><strong>data_type</strong> (<em>str</em>) – “image” or “video”.</p></li> <li><strong>dataset</strong> (<em>tuple</em>) – a 2-tuple containing (query, gallery), each of which contains
<li><p><strong>width</strong> (<em>int</em><em>, </em><em>optional</em>) – resized image width. Default is 128.</p></li> tuples of (img_path(s), pid, camid).</li>
<li><p><strong>height</strong> (<em>int</em><em>, </em><em>optional</em>) – resized image height. Default is 256.</p></li> <li><strong>data_type</strong> (<em>str</em>) – “image” or “video”.</li>
<li><p><strong>save_dir</strong> (<em>str</em>) – directory to save output images.</p></li> <li><strong>width</strong> (<em>int</em><em>, </em><em>optional</em>) – resized image width. Default is 128.</li>
<li><p><strong>topk</strong> (<em>int</em><em>, </em><em>optional</em>) – denoting top-k images in the rank list to be visualized. <li><strong>height</strong> (<em>int</em><em>, </em><em>optional</em>) – resized image height. Default is 256.</li>
Default is 10.</p></li> <li><strong>save_dir</strong> (<em>str</em>) – directory to save output images.</li>
<li><strong>topk</strong> (<em>int</em><em>, </em><em>optional</em>) – denoting top-k images in the rank list to be visualized.
Default is 10.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
</tbody>
</table>
</dd></dl> </dd></dl>
</div> </div>
@ -399,22 +425,27 @@ Default is 10.</p></li>
<span id="torch-tools"></span><h2>Torch Tools<a class="headerlink" href="#module-torchreid.utils.torchtools" title="Permalink to this headline"></a></h2> <span id="torch-tools"></span><h2>Torch Tools<a class="headerlink" href="#module-torchreid.utils.torchtools" title="Permalink to this headline"></a></h2>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.torchtools.save_checkpoint"> <dt id="torchreid.utils.torchtools.save_checkpoint">
<code class="sig-prename descclassname">torchreid.utils.torchtools.</code><code class="sig-name descname">save_checkpoint</code><span class="sig-paren">(</span><em class="sig-param">state</em>, <em class="sig-param">save_dir</em>, <em class="sig-param">is_best=False</em>, <em class="sig-param">remove_module_from_keys=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#save_checkpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.save_checkpoint" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.torchtools.</code><code class="descname">save_checkpoint</code><span class="sig-paren">(</span><em>state</em>, <em>save_dir</em>, <em>is_best=False</em>, <em>remove_module_from_keys=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#save_checkpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.save_checkpoint" title="Permalink to this definition"></a></dt>
<dd><p>Saves checkpoint.</p> <dd><p>Saves checkpoint.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>state</strong> (<em>dict</em>) – dictionary.</p></li> <tbody valign="top">
<li><p><strong>save_dir</strong> (<em>str</em>) – directory to save checkpoint.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>is_best</strong> (<em>bool</em><em>, </em><em>optional</em>) – if True, this checkpoint will be copied and named <li><strong>state</strong> (<em>dict</em>) – dictionary.</li>
<code class="docutils literal notranslate"><span class="pre">model-best.pth.tar</span></code>. Default is False.</p></li> <li><strong>save_dir</strong> (<em>str</em>) – directory to save checkpoint.</li>
<li><p><strong>remove_module_from_keys</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to remove “module.” <li><strong>is_best</strong> (<em>bool</em><em>, </em><em>optional</em>) – if True, this checkpoint will be copied and named
from layer names. Default is False.</p></li> <code class="docutils literal notranslate"><span class="pre">model-best.pth.tar</span></code>. Default is False.</li>
<li><strong>remove_module_from_keys</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to remove “module.”
from layer names. Default is False.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">state</span> <span class="o">=</span> <span class="p">{</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">state</span> <span class="o">=</span> <span class="p">{</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;state_dict&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;state_dict&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;epoch&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;epoch&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;rank1&#39;</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">,</span> <span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;rank1&#39;</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">,</span>
@ -429,20 +460,23 @@ from layer names. Default is False.</p></li>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.torchtools.load_checkpoint"> <dt id="torchreid.utils.torchtools.load_checkpoint">
<code class="sig-prename descclassname">torchreid.utils.torchtools.</code><code class="sig-name descname">load_checkpoint</code><span class="sig-paren">(</span><em class="sig-param">fpath</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#load_checkpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.load_checkpoint" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.torchtools.</code><code class="descname">load_checkpoint</code><span class="sig-paren">(</span><em>fpath</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#load_checkpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.load_checkpoint" title="Permalink to this definition"></a></dt>
<dd><p>Loads checkpoint.</p> <dd><p>Loads checkpoint.</p>
<p><code class="docutils literal notranslate"><span class="pre">UnicodeDecodeError</span></code> can be well handled, which means <p><code class="docutils literal notranslate"><span class="pre">UnicodeDecodeError</span></code> can be well handled, which means
python2-saved files can be read from python3.</p> python2-saved files can be read from python3.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><p><strong>fpath</strong> (<em>str</em>) – path to checkpoint.</p> <col class="field-body" />
</dd> <tbody valign="top">
<dt class="field-even">Returns</dt> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>fpath</strong> (<em>str</em>) – path to checkpoint.</td>
<dd class="field-even"><p>dict</p> </tr>
</dd> <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">dict</td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">load_checkpoint</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">load_checkpoint</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">fpath</span> <span class="o">=</span> <span class="s1">&#39;log/my_model/model.pth.tar-10&#39;</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">fpath</span> <span class="o">=</span> <span class="s1">&#39;log/my_model/model.pth.tar-10&#39;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">checkpoint</span> <span class="o">=</span> <span class="n">load_checkpoint</span><span class="p">(</span><span class="n">fpath</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">checkpoint</span> <span class="o">=</span> <span class="n">load_checkpoint</span><span class="p">(</span><span class="n">fpath</span><span class="p">)</span>
</pre></div> </pre></div>
@ -453,27 +487,32 @@ python2-saved files can be read from python3.</p>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.torchtools.resume_from_checkpoint"> <dt id="torchreid.utils.torchtools.resume_from_checkpoint">
<code class="sig-prename descclassname">torchreid.utils.torchtools.</code><code class="sig-name descname">resume_from_checkpoint</code><span class="sig-paren">(</span><em class="sig-param">fpath</em>, <em class="sig-param">model</em>, <em class="sig-param">optimizer=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#resume_from_checkpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.resume_from_checkpoint" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.torchtools.</code><code class="descname">resume_from_checkpoint</code><span class="sig-paren">(</span><em>fpath</em>, <em>model</em>, <em>optimizer=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#resume_from_checkpoint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.resume_from_checkpoint" title="Permalink to this definition"></a></dt>
<dd><p>Resumes training from a checkpoint.</p> <dd><p>Resumes training from a checkpoint.</p>
<p>This will load (1) model weights and (2) <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> <p>This will load (1) model weights and (2) <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>
of optimizer if <code class="docutils literal notranslate"><span class="pre">optimizer</span></code> is not None.</p> of optimizer if <code class="docutils literal notranslate"><span class="pre">optimizer</span></code> is not None.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>fpath</strong> (<em>str</em>) – path to checkpoint.</p></li> <tbody valign="top">
<li><p><strong>model</strong> (<em>nn.Module</em>) – model.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><p><strong>optimizer</strong> (<em>Optimizer</em><em>, </em><em>optional</em>) – an Optimizer.</p></li> <li><strong>fpath</strong> (<em>str</em>) – path to checkpoint.</li>
<li><strong>model</strong> (<em>nn.Module</em>) – model.</li>
<li><strong>optimizer</strong> (<em>Optimizer</em><em>, </em><em>optional</em>) – an Optimizer.</li>
</ul> </ul>
</dd> </td>
<dt class="field-even">Returns</dt> </tr>
<dd class="field-even"><p>start_epoch.</p> <tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">start_epoch.</p>
</dd> </td>
<dt class="field-odd">Return type</dt> </tr>
<dd class="field-odd"><p>int</p> <tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">int</p>
</dd> </td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">resume_from_checkpoint</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">resume_from_checkpoint</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">fpath</span> <span class="o">=</span> <span class="s1">&#39;log/my_model/model.pth.tar-10&#39;</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">fpath</span> <span class="o">=</span> <span class="s1">&#39;log/my_model/model.pth.tar-10&#39;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">start_epoch</span> <span class="o">=</span> <span class="n">resume_from_checkpoint</span><span class="p">(</span><span class="n">fpath</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">start_epoch</span> <span class="o">=</span> <span class="n">resume_from_checkpoint</span><span class="p">(</span><span class="n">fpath</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">)</span>
</pre></div> </pre></div>
@ -484,10 +523,11 @@ of optimizer if <code class="docutils literal notranslate"><span class="pre">opt
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.torchtools.open_all_layers"> <dt id="torchreid.utils.torchtools.open_all_layers">
<code class="sig-prename descclassname">torchreid.utils.torchtools.</code><code class="sig-name descname">open_all_layers</code><span class="sig-paren">(</span><em class="sig-param">model</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#open_all_layers"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.open_all_layers" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.torchtools.</code><code class="descname">open_all_layers</code><span class="sig-paren">(</span><em>model</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#open_all_layers"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.open_all_layers" title="Permalink to this definition"></a></dt>
<dd><p>Opens all layers in model for training.</p> <dd><p>Opens all layers in model for training.</p>
<dl> <dl class="docutils">
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">open_all_layers</span> <dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">open_all_layers</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">open_all_layers</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">open_all_layers</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
</pre></div> </pre></div>
</div> </div>
@ -497,19 +537,24 @@ of optimizer if <code class="docutils literal notranslate"><span class="pre">opt
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.torchtools.open_specified_layers"> <dt id="torchreid.utils.torchtools.open_specified_layers">
<code class="sig-prename descclassname">torchreid.utils.torchtools.</code><code class="sig-name descname">open_specified_layers</code><span class="sig-paren">(</span><em class="sig-param">model</em>, <em class="sig-param">open_layers</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#open_specified_layers"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.open_specified_layers" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.torchtools.</code><code class="descname">open_specified_layers</code><span class="sig-paren">(</span><em>model</em>, <em>open_layers</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#open_specified_layers"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.open_specified_layers" title="Permalink to this definition"></a></dt>
<dd><p>Opens specified layers in model for training while keeping <dd><p>Opens specified layers in model for training while keeping
other layers frozen.</p> other layers frozen.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>model</strong> (<em>nn.Module</em>) – neural net model.</p></li> <tbody valign="top">
<li><p><strong>open_layers</strong> (<em>str</em><em> or </em><em>list</em>) – layers open for training.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model</strong> (<em>nn.Module</em>) – neural net model.</li>
<li><strong>open_layers</strong> (<em>str</em><em> or </em><em>list</em>) – layers open for training.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">open_specified_layers</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">open_specified_layers</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Only model.classifier will be updated.</span> <span class="gp">&gt;&gt;&gt; </span><span class="c1"># Only model.classifier will be updated.</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">open_layers</span> <span class="o">=</span> <span class="s1">&#39;classifier&#39;</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">open_layers</span> <span class="o">=</span> <span class="s1">&#39;classifier&#39;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">open_specified_layers</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">open_layers</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">open_specified_layers</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">open_layers</span><span class="p">)</span>
@ -524,48 +569,58 @@ other layers frozen.</p>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.torchtools.count_num_param"> <dt id="torchreid.utils.torchtools.count_num_param">
<code class="sig-prename descclassname">torchreid.utils.torchtools.</code><code class="sig-name descname">count_num_param</code><span class="sig-paren">(</span><em class="sig-param">model</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#count_num_param"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.count_num_param" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.torchtools.</code><code class="descname">count_num_param</code><span class="sig-paren">(</span><em>model</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#count_num_param"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.count_num_param" title="Permalink to this definition"></a></dt>
<dd><p>Counts number of parameters in a model while ignoring <code class="docutils literal notranslate"><span class="pre">self.classifier</span></code>.</p> <dd><p>Counts number of parameters in a model while ignoring <code class="docutils literal notranslate"><span class="pre">self.classifier</span></code>.</p>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><p><strong>model</strong> (<em>nn.Module</em>) – network model.</p> <col class="field-body" />
</dd> <tbody valign="top">
</dl> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>model</strong> (<em>nn.Module</em>) – network model.</td>
<dl> </tr>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">count_num_param</span> </tbody>
</table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">count_num_param</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model_size</span> <span class="o">=</span> <span class="n">count_num_param</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">model_size</span> <span class="o">=</span> <span class="n">count_num_param</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
</pre></div> </pre></div>
</div> </div>
</dd> </dd>
</dl> </dl>
<div class="admonition warning"> <div class="admonition warning">
<p class="admonition-title">Warning</p> <p class="first admonition-title">Warning</p>
<p>This method is deprecated in favor of <p class="last">This method is deprecated in favor of
<code class="docutils literal notranslate"><span class="pre">torchreid.utils.compute_model_complexity</span></code>.</p> <code class="docutils literal notranslate"><span class="pre">torchreid.utils.compute_model_complexity</span></code>.</p>
</div> </div>
</dd></dl> </dd></dl>
<dl class="function"> <dl class="function">
<dt id="torchreid.utils.torchtools.load_pretrained_weights"> <dt id="torchreid.utils.torchtools.load_pretrained_weights">
<code class="sig-prename descclassname">torchreid.utils.torchtools.</code><code class="sig-name descname">load_pretrained_weights</code><span class="sig-paren">(</span><em class="sig-param">model</em>, <em class="sig-param">weight_path</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#load_pretrained_weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.load_pretrained_weights" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.torchtools.</code><code class="descname">load_pretrained_weights</code><span class="sig-paren">(</span><em>model</em>, <em>weight_path</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/torchtools.html#load_pretrained_weights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.torchtools.load_pretrained_weights" title="Permalink to this definition"></a></dt>
<dd><p>Loads pretrianed weights to model.</p> <dd><p>Loads pretrianed weights to model.</p>
<dl class="simple"> <dl class="docutils">
<dt>Features::</dt><dd><ul class="simple"> <dt>Features::</dt>
<li><p>Incompatible layers (unmatched in name or size) will be ignored.</p></li> <dd><ul class="first last simple">
<li><p>Can automatically deal with keys containing “module.”.</p></li> <li>Incompatible layers (unmatched in name or size) will be ignored.</li>
<li>Can automatically deal with keys containing “module.”.</li>
</ul> </ul>
</dd> </dd>
</dl> </dl>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>model</strong> (<em>nn.Module</em>) – network model.</p></li> <tbody valign="top">
<li><p><strong>weight_path</strong> (<em>str</em>) – path to pretrained weights.</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>model</strong> (<em>nn.Module</em>) – network model.</li>
<li><strong>weight_path</strong> (<em>str</em>) – path to pretrained weights.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">load_pretrained_weights</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">load_pretrained_weights</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">weight_path</span> <span class="o">=</span> <span class="s1">&#39;log/my_model/model-best.pth.tar&#39;</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">weight_path</span> <span class="o">=</span> <span class="s1">&#39;log/my_model/model-best.pth.tar&#39;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">load_pretrained_weights</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">weight_path</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">load_pretrained_weights</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">weight_path</span><span class="p">)</span>
</pre></div> </pre></div>
@ -576,32 +631,37 @@ other layers frozen.</p>
<span class="target" id="module-torchreid.utils.model_complexity"></span><dl class="function"> <span class="target" id="module-torchreid.utils.model_complexity"></span><dl class="function">
<dt id="torchreid.utils.model_complexity.compute_model_complexity"> <dt id="torchreid.utils.model_complexity.compute_model_complexity">
<code class="sig-prename descclassname">torchreid.utils.model_complexity.</code><code class="sig-name descname">compute_model_complexity</code><span class="sig-paren">(</span><em class="sig-param">model</em>, <em class="sig-param">input_size</em>, <em class="sig-param">verbose=False</em>, <em class="sig-param">only_conv_linear=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/model_complexity.html#compute_model_complexity"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.model_complexity.compute_model_complexity" title="Permalink to this definition"></a></dt> <code class="descclassname">torchreid.utils.model_complexity.</code><code class="descname">compute_model_complexity</code><span class="sig-paren">(</span><em>model</em>, <em>input_size</em>, <em>verbose=False</em>, <em>only_conv_linear=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/utils/model_complexity.html#compute_model_complexity"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.utils.model_complexity.compute_model_complexity" title="Permalink to this definition"></a></dt>
<dd><p>Returns number of parameters and FLOPs.</p> <dd><p>Returns number of parameters and FLOPs.</p>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>(1) this function only provides an estimate of the theoretical time complexity <p class="last">(1) this function only provides an estimate of the theoretical time complexity
rather than the actual running time which depends on implementations and hardware, rather than the actual running time which depends on implementations and hardware,
and (2) the FLOPs is only counted for layers that are used at test time. This means and (2) the FLOPs is only counted for layers that are used at test time. This means
that redundant layers such as person ID classification layer will be ignored as it that redundant layers such as person ID classification layer will be ignored as it
is discarded when doing feature extraction. Note that the inference graph depends on is discarded when doing feature extraction. Note that the inference graph depends on
how you construct the computations in <code class="docutils literal notranslate"><span class="pre">forward()</span></code>.</p> how you construct the computations in <code class="docutils literal notranslate"><span class="pre">forward()</span></code>.</p>
</div> </div>
<dl class="field-list simple"> <table class="docutils field-list" frame="void" rules="none">
<dt class="field-odd">Parameters</dt> <col class="field-name" />
<dd class="field-odd"><ul class="simple"> <col class="field-body" />
<li><p><strong>model</strong> (<em>nn.Module</em>) – network model.</p></li> <tbody valign="top">
<li><p><strong>input_size</strong> (<em>tuple</em>) – input size, e.g. (1, 3, 256, 128).</p></li> <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><p><strong>verbose</strong> (<em>bool</em><em>, </em><em>optional</em>) – shows detailed complexity of <li><strong>model</strong> (<em>nn.Module</em>) – network model.</li>
each module. Default is False.</p></li> <li><strong>input_size</strong> (<em>tuple</em>) – input size, e.g. (1, 3, 256, 128).</li>
<li><p><strong>only_conv_linear</strong> (<em>bool</em><em>, </em><em>optional</em>) – only considers convolution <li><strong>verbose</strong> (<em>bool</em><em>, </em><em>optional</em>) – shows detailed complexity of
each module. Default is False.</li>
<li><strong>only_conv_linear</strong> (<em>bool</em><em>, </em><em>optional</em>) – only considers convolution
and linear layers when counting flops. Default is True. and linear layers when counting flops. Default is True.
If set to False, flops of all layers will be counted.</p></li> If set to False, flops of all layers will be counted.</li>
</ul> </ul>
</dd> </td>
</dl> </tr>
<dl> </tbody>
<dt>Examples::</dt><dd><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">models</span><span class="p">,</span> <span class="n">utils</span> </table>
<dl class="docutils">
<dt>Examples::</dt>
<dd><div class="first last highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">models</span><span class="p">,</span> <span class="n">utils</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">build_model</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;resnet50&#39;</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">build_model</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;resnet50&#39;</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">num_params</span><span class="p">,</span> <span class="n">flops</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">compute_model_complexity</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="gp">&gt;&gt;&gt; </span><span class="n">num_params</span><span class="p">,</span> <span class="n">flops</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">compute_model_complexity</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div> </pre></div>

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@ -191,23 +191,23 @@
<h1>How-to<a class="headerlink" href="#how-to" title="Permalink to this headline"></a></h1> <h1>How-to<a class="headerlink" href="#how-to" title="Permalink to this headline"></a></h1>
<div class="contents local topic" id="contents"> <div class="contents local topic" id="contents">
<ul class="simple"> <ul class="simple">
<li><p><a class="reference internal" href="#find-model-keys" id="id1">Find model keys</a></p></li> <li><a class="reference internal" href="#find-model-keys" id="id1">Find model keys</a></li>
<li><p><a class="reference internal" href="#show-available-models" id="id2">Show available models</a></p></li> <li><a class="reference internal" href="#show-available-models" id="id2">Show available models</a></li>
<li><p><a class="reference internal" href="#change-the-training-sampler" id="id3">Change the training sampler</a></p></li> <li><a class="reference internal" href="#change-the-training-sampler" id="id3">Change the training sampler</a></li>
<li><p><a class="reference internal" href="#choose-an-optimizer-lr-scheduler" id="id4">Choose an optimizer/lr_scheduler</a></p></li> <li><a class="reference internal" href="#choose-an-optimizer-lr-scheduler" id="id4">Choose an optimizer/lr_scheduler</a></li>
<li><p><a class="reference internal" href="#resume-training" id="id5">Resume training</a></p></li> <li><a class="reference internal" href="#resume-training" id="id5">Resume training</a></li>
<li><p><a class="reference internal" href="#compute-model-complexity" id="id6">Compute model complexity</a></p></li> <li><a class="reference internal" href="#compute-model-complexity" id="id6">Compute model complexity</a></li>
<li><p><a class="reference internal" href="#combine-multiple-datasets" id="id7">Combine multiple datasets</a></p></li> <li><a class="reference internal" href="#combine-multiple-datasets" id="id7">Combine multiple datasets</a></li>
<li><p><a class="reference internal" href="#do-cross-dataset-evaluation" id="id8">Do cross-dataset evaluation</a></p></li> <li><a class="reference internal" href="#do-cross-dataset-evaluation" id="id8">Do cross-dataset evaluation</a></li>
<li><p><a class="reference internal" href="#combine-train-query-and-gallery" id="id9">Combine train, query and gallery</a></p></li> <li><a class="reference internal" href="#combine-train-query-and-gallery" id="id9">Combine train, query and gallery</a></li>
<li><p><a class="reference internal" href="#optimize-layers-with-different-learning-rates" id="id10">Optimize layers with different learning rates</a></p></li> <li><a class="reference internal" href="#optimize-layers-with-different-learning-rates" id="id10">Optimize layers with different learning rates</a></li>
<li><p><a class="reference internal" href="#do-two-stepped-transfer-learning" id="id11">Do two-stepped transfer learning</a></p></li> <li><a class="reference internal" href="#do-two-stepped-transfer-learning" id="id11">Do two-stepped transfer learning</a></li>
<li><p><a class="reference internal" href="#test-a-trained-model" id="id12">Test a trained model</a></p></li> <li><a class="reference internal" href="#test-a-trained-model" id="id12">Test a trained model</a></li>
<li><p><a class="reference internal" href="#visualize-learning-curves-with-tensorboard" id="id13">Visualize learning curves with tensorboard</a></p></li> <li><a class="reference internal" href="#visualize-learning-curves-with-tensorboard" id="id13">Visualize learning curves with tensorboard</a></li>
<li><p><a class="reference internal" href="#visualize-ranking-results" id="id14">Visualize ranking results</a></p></li> <li><a class="reference internal" href="#visualize-ranking-results" id="id14">Visualize ranking results</a></li>
<li><p><a class="reference internal" href="#visualize-activation-maps" id="id15">Visualize activation maps</a></p></li> <li><a class="reference internal" href="#visualize-activation-maps" id="id15">Visualize activation maps</a></li>
<li><p><a class="reference internal" href="#use-your-own-dataset" id="id16">Use your own dataset</a></p></li> <li><a class="reference internal" href="#use-your-own-dataset" id="id16">Use your own dataset</a></li>
<li><p><a class="reference internal" href="#design-your-own-engine" id="id17">Design your own Engine</a></p></li> <li><a class="reference internal" href="#design-your-own-engine" id="id17">Design your own Engine</a></li>
</ul> </ul>
</div> </div>
<div class="section" id="find-model-keys"> <div class="section" id="find-model-keys">
@ -384,14 +384,14 @@
<p>To understand where the CNN focuses on to extract features for ReID, you can visualize the activation maps as in <a class="reference external" href="https://arxiv.org/abs/1905.00953">OSNet</a>. This can be achieved by setting <code class="docutils literal notranslate"><span class="pre">visactmap=True</span></code> in <code class="docutils literal notranslate"><span class="pre">engine.run()</span></code> (<code class="docutils literal notranslate"><span class="pre">test_only</span></code> does not have to be True as <code class="docutils literal notranslate"><span class="pre">visactmap</span></code> is independent of <code class="docutils literal notranslate"><span class="pre">test_only</span></code>. See the code for details). Images will be saved in <code class="docutils literal notranslate"><span class="pre">save_dir/actmap_DATASETNAME</span></code>. An example is shown below (from left to right: image, activation map, overlapped image)</p> <p>To understand where the CNN focuses on to extract features for ReID, you can visualize the activation maps as in <a class="reference external" href="https://arxiv.org/abs/1905.00953">OSNet</a>. This can be achieved by setting <code class="docutils literal notranslate"><span class="pre">visactmap=True</span></code> in <code class="docutils literal notranslate"><span class="pre">engine.run()</span></code> (<code class="docutils literal notranslate"><span class="pre">test_only</span></code> does not have to be True as <code class="docutils literal notranslate"><span class="pre">visactmap</span></code> is independent of <code class="docutils literal notranslate"><span class="pre">test_only</span></code>. See the code for details). Images will be saved in <code class="docutils literal notranslate"><span class="pre">save_dir/actmap_DATASETNAME</span></code>. An example is shown below (from left to right: image, activation map, overlapped image)</p>
<a class="reference internal image-reference" href="_images/actmap.jpg"><img alt="_images/actmap.jpg" class="align-center" src="_images/actmap.jpg" style="width: 300px;" /></a> <a class="reference internal image-reference" href="_images/actmap.jpg"><img alt="_images/actmap.jpg" class="align-center" src="_images/actmap.jpg" style="width: 300px;" /></a>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>In order to visualize activation maps, the CNN needs to output the last convolutional feature maps at eval mode. See <code class="docutils literal notranslate"><span class="pre">torchreid/models/osnet.py</span></code> for example.</p> <p class="last">In order to visualize activation maps, the CNN needs to output the last convolutional feature maps at eval mode. See <code class="docutils literal notranslate"><span class="pre">torchreid/models/osnet.py</span></code> for example.</p>
</div> </div>
</div> </div>
<div class="section" id="use-your-own-dataset"> <div class="section" id="use-your-own-dataset">
<h2><a class="toc-backref" href="#id16">Use your own dataset</a><a class="headerlink" href="#use-your-own-dataset" title="Permalink to this headline"></a></h2> <h2><a class="toc-backref" href="#id16">Use your own dataset</a><a class="headerlink" href="#use-your-own-dataset" title="Permalink to this headline"></a></h2>
<ol class="arabic simple"> <ol class="arabic simple">
<li><p>Write your own dataset class. Below is a template for image dataset. However, it can also be applied to a video dataset class, for which you simply change <code class="docutils literal notranslate"><span class="pre">ImageDataset</span></code> to <code class="docutils literal notranslate"><span class="pre">VideoDataset</span></code>.</p></li> <li>Write your own dataset class. Below is a template for image dataset. However, it can also be applied to a video dataset class, for which you simply change <code class="docutils literal notranslate"><span class="pre">ImageDataset</span></code> to <code class="docutils literal notranslate"><span class="pre">VideoDataset</span></code>.</li>
</ol> </ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span> <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span> <span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
@ -432,14 +432,14 @@
</pre></div> </pre></div>
</div> </div>
<ol class="arabic simple" start="2"> <ol class="arabic simple" start="2">
<li><p>Register your dataset.</p></li> <li>Register your dataset.</li>
</ol> </ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchreid</span> <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchreid</span>
<span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">register_image_dataset</span><span class="p">(</span><span class="s1">&#39;new_dataset&#39;</span><span class="p">,</span> <span class="n">NewDataset</span><span class="p">)</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">register_image_dataset</span><span class="p">(</span><span class="s1">&#39;new_dataset&#39;</span><span class="p">,</span> <span class="n">NewDataset</span><span class="p">)</span>
</pre></div> </pre></div>
</div> </div>
<ol class="arabic simple" start="3"> <ol class="arabic simple" start="3">
<li><p>Initialize a data manager with your dataset.</p></li> <li>Initialize a data manager with your dataset.</li>
</ol> </ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># use your own dataset only</span> <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># use your own dataset only</span>
<span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ImageDataManager</span><span class="p">(</span> <span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ImageDataManager</span><span class="p">(</span>