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Models","anchor":"pretrained-models","htmlText":"Pretrained Models"},{"level":2,"text":"Perform training on Scene Graph Generation","anchor":"perform-training-on-scene-graph-generation","htmlText":"Perform training on Scene Graph Generation"},{"level":3,"text":"Set the dataset path","anchor":"set-the-dataset-path","htmlText":"Set the dataset path"},{"level":3,"text":"Choose a dataset","anchor":"choose-a-dataset","htmlText":"Choose a dataset"},{"level":3,"text":"Choose a task","anchor":"choose-a-task","htmlText":"Choose a task"},{"level":3,"text":"Choose your model","anchor":"choose-your-model","htmlText":"Choose your model"},{"level":3,"text":"Choose your Encoder","anchor":"choose-your-encoder","htmlText":"Choose your Encoder"},{"level":3,"text":"Examples of the Training Command","anchor":"examples-of-the-training-command","htmlText":"Examples of the Training 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class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph Generation in Pytorch<a id="user-content-environment-invariant-curriculum-relation-learning-for-fine-grained-scene-graph-generation-in-pytorch" class="anchor-element" aria-label="Permalink: Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph Generation in Pytorch" href="#environment-invariant-curriculum-relation-learning-for-fine-grained-scene-graph-generation-in-pytorch"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation<a id="user-content-installation" class="anchor-element" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<p dir="auto">Check <a href="/myukzzz/EICR/blob/main/INSTALL.md">INSTALL.md for installation instructions, the recommended configuration is cuda-10.1 & pytorch-1.7.1.

\n<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Dataset<a id="user-content-dataset" class="anchor-element" aria-label="Permalink: Dataset" href="#dataset"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<p dir="auto">Check <a href="/myukzzz/EICR/blob/main/DATASET.md">DATASET.md for instructions of dataset preprocessing (VG & GQA).

\n<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Pretrained Models<a id="user-content-pretrained-models" class="anchor-element" aria-label="Permalink: Pretrained Models" href="#pretrained-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<p dir="auto">For VG dataset, the pretrained object detector we used is provided by <a href="https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch\">Scene-Graph-Benchmark

\n<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Perform training on Scene Graph Generation<a id="user-content-perform-training-on-scene-graph-generation" class="anchor-element" aria-label="Permalink: Perform training on Scene Graph Generation" href="#perform-training-on-scene-graph-generation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Set the dataset path<a id="user-content-set-the-dataset-path" class="anchor-element" aria-label="Permalink: Set the dataset path" href="#set-the-dataset-path"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<p dir="auto">First, organize all the files like this:

\n<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="datasets\n |-- vg\n |--detector_model\n |--pretrained_faster_rcnn\n |--model_final.pth\n |--GQA\n |--model_final_from_vg.pth \n |--glove\n |--.... (glove files, will autoly download)\n |--VG_100K\n |--.... (images)\n |--VG-SGG-with-attri.h5 \n |--VG-SGG-dicts-with-attri.json\n |--image_data.json \n |--gqa\n |--images\n |--.... (images)\n |--GQA_200_ID_Info.json\n |--GQA_200_Train.json\n |--GQA_200_Test.json">

datasets\n  <span class="pl-k">|-- vg\n    <span class="pl-k">|--detector_model\n      <span class="pl-k">|--pretrained_faster_rcnn\n        <span class="pl-k">|--model_final.pth\n      <span class="pl-k">|--GQA\n        <span class="pl-k">|--model_final_from_vg.pth       \n    <span class="pl-k">|--glove\n      <span class="pl-k">|--.... (glove files, will autoly download)\n    <span class="pl-k">|--VG_100K\n      <span class="pl-k">|--.... (images)\n    <span class="pl-k">|--VG-SGG-with-attri.h5 \n    <span class="pl-k">|--VG-SGG-dicts-with-attri.json\n    <span class="pl-k">|--image_data.json    \n  <span class="pl-k">|--gqa\n    <span class="pl-k">|--images\n      <span class="pl-k">|--.... (images)\n    <span class="pl-k">|--GQA_200_ID_Info.json\n    <span class="pl-k">|--GQA_200_Train.json\n    <span class="pl-k">|--GQA_200_Test.json

\n<div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Choose a dataset<a id="user-content-choose-a-dataset" class="anchor-element" aria-label="Permalink: Choose a dataset" href="#choose-a-dataset"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<p dir="auto">You can choose the training/testing dataset by setting the following parameter:

\n<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="GLOBAL_SETTING.DATASET_CHOICE 'VG' #['VG', 'GQA']">

GLOBAL_SETTING.DATASET_CHOICE <span class="pl-s"><span class="pl-pds">'VG<span class="pl-pds">'  <span class="pl-c"><span class="pl-c">#['VG', 'GQA']

\n<div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Choose a task<a id="user-content-choose-a-task" class="anchor-element" aria-label="Permalink: Choose a task" href="#choose-a-task"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<p dir="auto">To comprehensively evaluate the performance, we follow three conventional tasks: 1) Predicate Classification (PredCls) predicts the relationships of all the pairwise objects by employing the given ground-truth bounding boxes and classes; 2) Scene Graph Classification (SGCls) predicts the objects classes and their pairwise relationships by employing the given ground-truth object bounding boxes; and 3) Scene Graph Detection (SGDet) detects all the objects in an image, and predicts their bounding boxes, classes, and pairwise relationships.

\n<p dir="auto">For Predicate Classification (PredCls), you need to set:

\n<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL True">

MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL True

\n<p dir="auto">For Scene Graph Classification (SGCls):

\n<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False">

MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False

\n<p dir="auto">For Scene Graph Detection (SGDet):

\n<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False">

MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False

\n<div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Choose your model<a id="user-content-choose-your-model" class="anchor-element" aria-label="Permalink: Choose your model" href="#choose-your-model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<p dir="auto">We abstract various SGG models to be different relation-head predictors in the file roi_heads/relation_head/roi_relation_predictors.py, which are independent of the Faster R-CNN backbone and relation-head feature extractor. You can use GLOBAL_SETTING.RELATION_PREDICTOR to select one of them:

\n<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="GLOBAL_SETTING.RELATION_PREDICTOR 'EICR_model'">

GLOBAL_SETTING.RELATION_PREDICTOR <span class="pl-s"><span class="pl-pds">'EICR_model<span class="pl-pds">'

\n<p dir="auto">The default settings are under configs/SHA_GCL_e2e_relation_X_101_32_8_FPN_1x.yaml and maskrcnn_benchmark/config/defaults.py. The priority is command > yaml > defaults.py.

\n<div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Choose your Encoder<a id="user-content-choose-your-encoder" class="anchor-element" aria-label="Permalink: Choose your Encoder" href="#choose-your-encoder"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<p dir="auto">You need to further choose an object/relation encoder for "Motifs" or "VCTree" or "Self-Attention" predictor, by setting the following parameter:

\n<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="GLOBAL_SETTING.BASIC_ENCODER 'Motifs'">

GLOBAL_SETTING.BASIC_ENCODER <span class="pl-s"><span class="pl-pds">'Motifs<span class="pl-pds">'

\n<div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Examples of the Training Command<a id="user-content-examples-of-the-training-command" class="anchor-element" aria-label="Permalink: Examples of the Training Command" href="#examples-of-the-training-command"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<p dir="auto">Training Example 1 : (VG, Motifs, PredCls)

\n<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10050 --nproc_per_node=1 ./tools/relation_train_net.py --config-file "configs/SHA_GCL_e2e_relation_X_101_32_8_FPN_1x.yaml" GLOBAL_SETTING.DATASET_CHOICE 'VG' GLOBAL_SETTING.RELATION_PREDICTOR 'EICR_model' GLOBAL_SETTING.BASIC_ENCODER 'Motifs' GLOBAL_SETTING.GCL_SETTING.GROUP_SPLIT_MODE 'divide4' GLOBAL_SETTING.GCL_SETTING.KNOWLEDGE_TRANSFER_MODE 'KL_logit_TopDown' MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL True SOLVER.IMS_PER_BATCH 4 TEST.IMS_PER_BATCH 1 DTYPE "float16" SOLVER.MAX_ITER 120000 SOLVER.VAL_PERIOD 10000 SOLVER.CHECKPOINT_PERIOD 10000 GLOVE_DIR /data/myk/newreason/SHA/datasets/vg OUTPUT_DIR /data/myk/newreason/ICCV23/SHA/output/VG_predcls_EICR SOLVER.SCHEDULE.TYPE WarmupMultiStepLR SOLVER.STEPS "(56000, 96000)"">

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10050 --nproc_per_node=1 ./tools/relation_train_net.py --config-file <span class="pl-s"><span class="pl-pds">"configs/SHA_GCL_e2e_relation_X_101_32_8_FPN_1x.yaml<span class="pl-pds">" GLOBAL_SETTING.DATASET_CHOICE <span class="pl-s"><span class="pl-pds">'VG<span class="pl-pds">' GLOBAL_SETTING.RELATION_PREDICTOR <span class="pl-s"><span class="pl-pds">'EICR_model<span class="pl-pds">' GLOBAL_SETTING.BASIC_ENCODER <span class="pl-s"><span class="pl-pds">'Motifs<span class="pl-pds">' GLOBAL_SETTING.GCL_SETTING.GROUP_SPLIT_MODE <span class="pl-s"><span class="pl-pds">'divide4<span class="pl-pds">' GLOBAL_SETTING.GCL_SETTING.KNOWLEDGE_TRANSFER_MODE <span class="pl-s"><span class="pl-pds">'KL_logit_TopDown<span class="pl-pds">' MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL True SOLVER.IMS_PER_BATCH 4 TEST.IMS_PER_BATCH 1 DTYPE <span class="pl-s"><span class="pl-pds">"float16<span class="pl-pds">" SOLVER.MAX_ITER 120000 SOLVER.VAL_PERIOD 10000 SOLVER.CHECKPOINT_PERIOD 10000 GLOVE_DIR /data/myk/newreason/SHA/datasets/vg OUTPUT_DIR /data/myk/newreason/ICCV23/SHA/output/VG_predcls_EICR SOLVER.SCHEDULE.TYPE WarmupMultiStepLR    SOLVER.STEPS <span class="pl-s"><span class="pl-pds">"(56000, 96000)<span class="pl-pds">"

\n<p dir="auto">Training Example 2 : (GQA_200, Motifs, SGCls)

\n<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10050 --nproc_per_node=1 ./tools/relation_train_net.py --config-file "configs/SHA_GCL_e2e_relation_X_101_32_8_FPN_1x.yaml" GLOBAL_SETTING.DATASET_CHOICE 'GQA_200' GLOBAL_SETTING.RELATION_PREDICTOR 'EICR_model' GLOBAL_SETTING.BASIC_ENCODER 'Motifs' GLOBAL_SETTING.GCL_SETTING.GROUP_SPLIT_MODE 'divide4' GLOBAL_SETTING.GCL_SETTING.KNOWLEDGE_TRANSFER_MODE 'KL_logit_TopDown' MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False SOLVER.IMS_PER_BATCH 4 TEST.IMS_PER_BATCH 1 DTYPE "float16" SOLVER.MAX_ITER 120000 SOLVER.VAL_PERIOD 10000 SOLVER.CHECKPOINT_PERIOD 10000 GLOVE_DIR /data/myk/newreason/SHA/datasets/vg OUTPUT_DIR /data/myk/newreason/ICCV23/SHA/output/VG_predcls_EICR SOLVER.SCHEDULE.TYPE WarmupMultiStepLR SOLVER.STEPS "(56000, 96000)"">

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10050 --nproc_per_node=1 ./tools/relation_train_net.py --config-file <span class="pl-s"><span class="pl-pds">"configs/SHA_GCL_e2e_relation_X_101_32_8_FPN_1x.yaml<span class="pl-pds">" GLOBAL_SETTING.DATASET_CHOICE <span class="pl-s"><span class="pl-pds">'GQA_200<span class="pl-pds">' GLOBAL_SETTING.RELATION_PREDICTOR <span class="pl-s"><span class="pl-pds">'EICR_model<span class="pl-pds">' GLOBAL_SETTING.BASIC_ENCODER <span class="pl-s"><span class="pl-pds">'Motifs<span class="pl-pds">' GLOBAL_SETTING.GCL_SETTING.GROUP_SPLIT_MODE <span class="pl-s"><span class="pl-pds">'divide4<span class="pl-pds">' GLOBAL_SETTING.GCL_SETTING.KNOWLEDGE_TRANSFER_MODE <span class="pl-s"><span class="pl-pds">'KL_logit_TopDown<span class="pl-pds">' MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False SOLVER.IMS_PER_BATCH 4 TEST.IMS_PER_BATCH 1 DTYPE <span class="pl-s"><span class="pl-pds">"float16<span class="pl-pds">" SOLVER.MAX_ITER 120000 SOLVER.VAL_PERIOD 10000 SOLVER.CHECKPOINT_PERIOD 10000 GLOVE_DIR /data/myk/newreason/SHA/datasets/vg OUTPUT_DIR /data/myk/newreason/ICCV23/SHA/output/VG_predcls_EICR SOLVER.SCHEDULE.TYPE WarmupMultiStepLR    SOLVER.STEPS <span class="pl-s"><span class="pl-pds">"(56000, 96000)<span class="pl-pds">"

\n<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Evaluation<a id="user-content-evaluation" class="anchor-element" aria-label="Permalink: Evaluation" href="#evaluation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<p dir="auto">You can evaluate it by running the following command.

\n<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10083 --nproc_per_node=1 tools/relation_test_net.py --config-file "configs/SHA_GCL_e2e_relation_X_101_32_8_FPN_1x.yaml" GLOBAL_SETTING.DATASET_CHOICE 'GQA_200' GLOBAL_SETTING.RELATION_PREDICTOR 'EICR_model' GLOBAL_SETTING.BASIC_ENCODER 'Motifs' GLOBAL_SETTING.GCL_SETTING.GROUP_SPLIT_MODE 'divide4' GLOBAL_SETTING.GCL_SETTING.KNOWLEDGE_TRANSFER_MODE 'KL_logit_TopDown' MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False TEST.IMS_PER_BATCH 1 DTYPE "float16" GLOVE_DIR /home/myk/home/reason/newreason/SHA/datasets/vg/glove OUTPUT_DIR /home/myk/home/reason/newreason/SHA/output/GQA_precl_motif3samples_09aplha_start30000end60000/">

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10083 --nproc_per_node=1 tools/relation_test_net.py --config-file <span class="pl-s"><span class="pl-pds">"configs/SHA_GCL_e2e_relation_X_101_32_8_FPN_1x.yaml<span class="pl-pds">" GLOBAL_SETTING.DATASET_CHOICE <span class="pl-s"><span class="pl-pds">'GQA_200<span class="pl-pds">' GLOBAL_SETTING.RELATION_PREDICTOR <span class="pl-s"><span class="pl-pds">'EICR_model<span class="pl-pds">' GLOBAL_SETTING.BASIC_ENCODER <span class="pl-s"><span class="pl-pds">'Motifs<span class="pl-pds">' GLOBAL_SETTING.GCL_SETTING.GROUP_SPLIT_MODE <span class="pl-s"><span class="pl-pds">'divide4<span class="pl-pds">' GLOBAL_SETTING.GCL_SETTING.KNOWLEDGE_TRANSFER_MODE <span class="pl-s"><span class="pl-pds">'KL_logit_TopDown<span class="pl-pds">' MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False TEST.IMS_PER_BATCH 1 DTYPE <span class="pl-s"><span class="pl-pds">"float16<span class="pl-pds">" GLOVE_DIR /home/myk/home/reason/newreason/SHA/datasets/vg/glove OUTPUT_DIR /home/myk/home/reason/newreason/SHA/output/GQA_precl_motif3samples_09aplha_start30000end60000/

\n<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation<a id="user-content-citation" class="anchor-element" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@inproceedings{min2023environment,\n title={Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph Generation},\n author={Min, Yukuan and Wu, Aming and Deng, Cheng},\n booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},\n pages={13296--13307},\n year={2023}\n}">

@inproceedings{min2023environment,\n  title={Environment-Invariant Curriculum Relation Learning <span class="pl-k">for Fine-Grained Scene Graph Generation},\n  author={Min, Yukuan and Wu, Aming and Deng, Cheng},\n  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},\n  pages={13296--13307},\n  year={2023}\n}

\n<div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Acknowledgment<a id="user-content-acknowledgment" class="anchor-element" aria-label="Permalink: Acknowledgment" href="#acknowledgment"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z">\n<p dir="auto">Our code is on top of <a href="https://github.com/dongxingning/SHA-GCL-for-SGG\">SHA-GCL-for-SGG, we sincerely thank them for their well-designed codebase.

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