Update doc

Author: lizekai-richard

book/augmentations/cutmix.html

@@ -158,8 +158,8 @@ <h2 id="cutmix"><a class="header" href="#cutmix">Cutmix</a></h2>
 <p>Cutmix is a data augmentation technique that creates new samples by combining patches from two images while blending their labels proportionally to the area of the patches.. We follow the implementation of cutmix in <a href="https://github.com/VILA-Lab/SRe2L/tree/main/SRe2L">SRe2L</a>.</p>
 <div style="background-color:#F7F7F7; padding:15px; border:1px solid #E0E0E0; border-top:3px solid #FF0000; font-family:monospace; font-size:14px;">
 <p><span style="color:#FF6B00;">CLASS</span>
-dd_ranking.aug.Cutmix(params: dict)
-<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/aug/cutmix.py"><strong>[SOURCE]</strong></a></p>
+ddranking.aug.Cutmix(params: dict)
+<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/aug/cutmix.py"><strong>[SOURCE]</strong></a></p>
 </div>
 <h3 id="parameters"><a class="header" href="#parameters">Parameters</a></h3>
 <ul>

book/augmentations/dsa.html

@@ -169,8 +169,8 @@ <h2 id="differentiable-siamese-augmentation-dsa"><a class="header" href="#differ
 </ul>
 <div style="background-color:#F7F7F7; padding:15px; border:1px solid #E0E0E0; border-top:3px solid #FF0000; font-family:monospace; font-size:14px;">
 <p><span style="color:#FF6B00;">CLASS</span>
-dd_ranking.aug.DSA(params: dict, seed: int, aug_mode: str)
-<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/aug/dsa.py"><strong>[SOURCE]</strong></a></p>
+ddranking.aug.DSA(params: dict, seed: int, aug_mode: str)
+<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/aug/dsa.py"><strong>[SOURCE]</strong></a></p>
 </div>
 <h3 id="parameters"><a class="header" href="#parameters">Parameters</a></h3>
 <ul>

book/augmentations/mixup.html

@@ -158,8 +158,8 @@ <h2 id="mixup"><a class="header" href="#mixup">Mixup</a></h2>
 <p>Mixup is a data augmentation technique that generates new training samples by linearly interpolating pairs of images. We follow the implementation of mixup in <a href="https://github.com/VILA-Lab/SRe2L/tree/main/SRe2L">SRe2L</a>.</p>
 <div style="background-color:#F7F7F7; padding:15px; border:1px solid #E0E0E0; border-top:3px solid #FF0000; font-family:monospace; font-size:14px;">
 <p><span style="color:#FF6B00;">CLASS</span>
-dd_ranking.aug.Mixup(params: dict)
-<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/aug/mixup.py"><strong>[SOURCE]</strong></a></p>
+ddranking.aug.Mixup(params: dict)
+<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/aug/mixup.py"><strong>[SOURCE]</strong></a></p>
 </div>
 <h3 id="parameters"><a class="header" href="#parameters">Parameters</a></h3>
 <ul>

book/datasets/overview.html

@@ -157,8 +157,8 @@ <h1 class="menu-title">DD-Ranking API Documentation</h1>
                         <h1 id="datasets"><a class="header" href="#datasets">Datasets</a></h1>
 <p>DD-Ranking provides a set of commonly used datasets in existing dataset distillation methods. Users can flexibly use these datasets for evaluation. The interface to load datasets is as follows:</p>
 <div style="background-color:#F7F7F7; padding:15px; border:1px solid #E0E0E0; border-top:3px solid #FF0000; font-family:monospace; font-size:14px;">
-<p>dd_ranking.utils.get_dataset(dataset: str, data_path: str, im_size: tuple, use_zca: bool, custom_val_trans: Optional[Callable], device: str)
-<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/utils/data.py"><strong>[SOURCE]</strong></a></p>
+<p>ddranking.utils.get_dataset(dataset: str, data_path: str, im_size: tuple, use_zca: bool, custom_val_trans: Optional[Callable], device: str)
+<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/utils/data.py"><strong>[SOURCE]</strong></a></p>
 </div>
 <h3 id="parameters"><a class="header" href="#parameters">Parameters</a></h3>
 <ul>

book/metrics/general.html

@@ -187,7 +187,7 @@ <h2 id="generalevaluator"><a class="header" href="#generalevaluator">GeneralEval
 custom_val_trans: Optional[Callable]=None,
 device: str="cuda"
 )
-<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/metrics/general.py"><strong>[SOURCE]</strong></a></p>
+<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/metrics/general.py"><strong>[SOURCE]</strong></a></p>
 </div>
 <p>A class for evaluating the traditional test accuracy of a surrogate model on the synthetic dataset under various settings (label type, data augmentation, etc.).</p>
 <h3 id="parameters"><a class="header" href="#parameters">Parameters</a></h3>

book/metrics/hard-label.html

@@ -182,7 +182,7 @@ <h2 id="hardlabelevaluator"><a class="header" href="#hardlabelevaluator">HardLab
 custom_val_trans = None,
 device: str = "cuda"
 )
-<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/metrics/hard_label.py"><strong>[SOURCE]</strong></a></p>
+<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/metrics/hard_label.py"><strong>[SOURCE]</strong></a></p>
 </div>
 <p>A class for evaluating the performance of a dataset distillation method with hard labels. User is able to modify the attributes as needed.</p>
 <h3 id="parameters"><a class="header" href="#parameters">Parameters</a></h3>

book/metrics/soft-label.html

@@ -187,7 +187,7 @@ <h2 id="softlabelevaluator"><a class="header" href="#softlabelevaluator">SoftLab
 custom_val_trans: Optional[Callable]=None,
 device: str="cuda"
 )
-<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/metrics/soft_label.py"><strong>[SOURCE]</strong></a></p>
+<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/metrics/soft_label.py"><strong>[SOURCE]</strong></a></p>
 </div>
 <p>A class for evaluating the performance of a dataset distillation method with soft labels. User is able to modify the attributes as needed.</p>
 <h3 id="parameters"><a class="header" href="#parameters">Parameters</a></h3>

book/models/alexnet.html

@@ -155,11 +155,11 @@ <h1 class="menu-title">DD-Ranking API Documentation</h1>
                 <div id="content" class="content">
                     <main>
                         <h2 id="alexnet"><a class="header" href="#alexnet">AlexNet</a></h2>
-<p>Our <a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/utils/networks.py">implementation</a> of ConvNet is based on <a href="https://github.com/VICO-UoE/DatasetCondensation">DC</a>.</p>
+<p>Our <a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/utils/networks.py">implementation</a> of ConvNet is based on <a href="https://github.com/VICO-UoE/DatasetCondensation">DC</a>.</p>
 <p>We provide the following interface to initialize a AlexNet model:</p>
 <div style="background-color:#F7F7F7; padding:15px; border:1px solid #E0E0E0; border-top:3px solid #FF0000; font-family:monospace; font-size:14px;">
-<p>dd_ranking.utils.get_alexnet(model_name: str, im_size: tuple, channel: int, num_classes: int, pretrained: bool, model_path: str)
-<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/utils/model.py"><strong>[SOURCE]</strong></a></p>
+<p>ddranking.utils.get_alexnet(model_name: str, im_size: tuple, channel: int, num_classes: int, pretrained: bool, model_path: str)
+<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/utils/model.py"><strong>[SOURCE]</strong></a></p>
 </div>
 <h3 id="parameters"><a class="header" href="#parameters">Parameters</a></h3>
 <ul>

book/models/convnet.html

@@ -155,12 +155,12 @@ <h1 class="menu-title">DD-Ranking API Documentation</h1>
                 <div id="content" class="content">
                     <main>
                         <h2 id="convnet"><a class="header" href="#convnet">ConvNet</a></h2>
-<p>Our <a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/utils/networks.py">implementation</a> of ConvNet is based on <a href="https://github.com/VICO-UoE/DatasetCondensation">DC</a>.</p>
+<p>Our <a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/utils/networks.py">implementation</a> of ConvNet is based on <a href="https://github.com/VICO-UoE/DatasetCondensation">DC</a>.</p>
 <p>By default, we use width 128, average pooling, and ReLU activation. We provide the following interface to initialize a ConvNet model:</p>
 <div style="background-color:#F7F7F7; padding:15px; border:1px solid #E0E0E0; border-top:3px solid #FF0000; font-family:monospace; font-size:14px;">
 <p>dd_ranking.utils.get_convnet(model_name: str,
 im_size: tuple, channel: int, num_classes: int, net_depth: int, net_norm: str, pretrained: bool, model_path: str)
-<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/utils/model.py"><strong>[SOURCE]</strong></a></p>
+<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/utils/model.py"><strong>[SOURCE]</strong></a></p>
 </div>
 <h3 id="parameters"><a class="header" href="#parameters">Parameters</a></h3>
 <ul>

book/models/lenet.html

@@ -155,11 +155,11 @@ <h1 class="menu-title">DD-Ranking API Documentation</h1>
                 <div id="content" class="content">
                     <main>
                         <h2 id="lenet"><a class="header" href="#lenet">LeNet</a></h2>
-<p>Our <a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/utils/networks.py">implementation</a> of LeNet is based on <a href="https://github.com/VICO-UoE/DatasetCondensation">DC</a>.</p>
+<p>Our <a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/utils/networks.py">implementation</a> of LeNet is based on <a href="https://github.com/VICO-UoE/DatasetCondensation">DC</a>.</p>
 <p>We provide the following interface to initialize a LeNet model:</p>
 <div style="background-color:#F7F7F7; padding:15px; border:1px solid #E0E0E0; border-top:3px solid #FF0000; font-family:monospace; font-size:14px;">
-<p>dd_ranking.utils.get_lenet(model_name: str, im_size: tuple, channel: int, num_classes: int, pretrained: bool, model_path: str)
-<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/dd_ranking/utils/model.py"><strong>[SOURCE]</strong></a></p>
+<p>ddranking.utils.get_lenet(model_name: str, im_size: tuple, channel: int, num_classes: int, pretrained: bool, model_path: str)
+<a href="https://github.com/NUS-HPC-AI-Lab/DD-Ranking/blob/main/ddranking/utils/model.py"><strong>[SOURCE]</strong></a></p>
 </div>
 <h3 id="parameters"><a class="header" href="#parameters">Parameters</a></h3>
 <ul>