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@@ -0,0 +1 @@ +/ShapeY-0.1.8.tar.gz diff --git a/python-shapey.spec b/python-shapey.spec new file mode 100644 index 0000000..1366f56 --- /dev/null +++ b/python-shapey.spec @@ -0,0 +1,174 @@ +%global _empty_manifest_terminate_build 0 +Name: python-ShapeY +Version: 0.1.8 +Release: 1 +Summary: Benchmark that tests shape recognition +License: MIT +URL: https://github.com/njw0709/ShapeY +Source0: https://mirrors.aliyun.com/pypi/web/packages/3d/83/3a9496be1f7487ffec581d96d96f49b4dc20b5aa462d3aadb158da579008/ShapeY-0.1.8.tar.gz +BuildArch: noarch + + +%description +# ShapeY + +ShapeY is a benchmark that tests a vision system's shape recognition capacity. ShapeY currently consists of ~68k images of 200 3D objects taken from ShapeNet. Note that this benchmark is not meant to be used as a training dataset, but rather serves to validate that the visual object recogntion / classification under inspection has developed a capacity to perform well on our benchmarking tasks, which are designed to be hard if the system does not understand shape. + +## Installing ShapeY +Requirements: Python 3, Cuda version 10.2 (prerequisite for cupy) + +To install ShapeY, run the following command: +``` +pip3 install shapey==0.1.7 +``` + +## Step0: Download ShapeY200 dataset +Run `download.sh` to download the dataset. The script automatically unzips the images under `data/ShapeY200/`. +Downloading uses gdown, which is google drive command line tool. If it does not work, please just follow the two links down below to download the ShapeY200 / ShapeY200CR datasets. + +ShapeY200: +https://drive.google.com/uc?id=1arDu0c9hYLHVMiB52j_a-e0gVnyQfuQV + +ShapeY200CR: +https://drive.google.com/uc?id=1WXpNUVRn6D0F9T3IHruml2DcDCFRsix- + +After downloading the two datasets, move each of them to the `data/` directory. For example, all of the images for ShapeY200 should be under `data/ShapeY200/dataset/`. + +## Step1: Extract the embedding vectors from your own vision model using our dataset +Implement the function `your_feature_output_code` in `step1_save_feature/your_feature_extraction_code.py`. The function should take in the path to the dataset as input and return two lists - one for the image names and another for the corresponding embedding vectors taken from whatever system. + +## Step2: Run macro.py +After you have implemented the function, run `macro.py` to generate the results. +`macro.py` will automatically run the following steps: +1. Compute correlation between all embedding vectors (using `step2_compute_feature_correlation/compute_correlation.py`) + +2. Run benchmark analysis (using `step3_benchmark_analysis/get_nn_classification_error_with_exclusion_distance.py`) + +3. Graph results (top1 object matching error, top1 category matching error, etc.) + +%package -n python3-ShapeY +Summary: Benchmark that tests shape recognition +Provides: python-ShapeY +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-ShapeY +# ShapeY + +ShapeY is a benchmark that tests a vision system's shape recognition capacity. ShapeY currently consists of ~68k images of 200 3D objects taken from ShapeNet. Note that this benchmark is not meant to be used as a training dataset, but rather serves to validate that the visual object recogntion / classification under inspection has developed a capacity to perform well on our benchmarking tasks, which are designed to be hard if the system does not understand shape. + +## Installing ShapeY +Requirements: Python 3, Cuda version 10.2 (prerequisite for cupy) + +To install ShapeY, run the following command: +``` +pip3 install shapey==0.1.7 +``` + +## Step0: Download ShapeY200 dataset +Run `download.sh` to download the dataset. The script automatically unzips the images under `data/ShapeY200/`. +Downloading uses gdown, which is google drive command line tool. If it does not work, please just follow the two links down below to download the ShapeY200 / ShapeY200CR datasets. + +ShapeY200: +https://drive.google.com/uc?id=1arDu0c9hYLHVMiB52j_a-e0gVnyQfuQV + +ShapeY200CR: +https://drive.google.com/uc?id=1WXpNUVRn6D0F9T3IHruml2DcDCFRsix- + +After downloading the two datasets, move each of them to the `data/` directory. For example, all of the images for ShapeY200 should be under `data/ShapeY200/dataset/`. + +## Step1: Extract the embedding vectors from your own vision model using our dataset +Implement the function `your_feature_output_code` in `step1_save_feature/your_feature_extraction_code.py`. The function should take in the path to the dataset as input and return two lists - one for the image names and another for the corresponding embedding vectors taken from whatever system. + +## Step2: Run macro.py +After you have implemented the function, run `macro.py` to generate the results. +`macro.py` will automatically run the following steps: +1. Compute correlation between all embedding vectors (using `step2_compute_feature_correlation/compute_correlation.py`) + +2. Run benchmark analysis (using `step3_benchmark_analysis/get_nn_classification_error_with_exclusion_distance.py`) + +3. Graph results (top1 object matching error, top1 category matching error, etc.) + +%package help +Summary: Development documents and examples for ShapeY +Provides: python3-ShapeY-doc +%description help +# ShapeY + +ShapeY is a benchmark that tests a vision system's shape recognition capacity. ShapeY currently consists of ~68k images of 200 3D objects taken from ShapeNet. Note that this benchmark is not meant to be used as a training dataset, but rather serves to validate that the visual object recogntion / classification under inspection has developed a capacity to perform well on our benchmarking tasks, which are designed to be hard if the system does not understand shape. + +## Installing ShapeY +Requirements: Python 3, Cuda version 10.2 (prerequisite for cupy) + +To install ShapeY, run the following command: +``` +pip3 install shapey==0.1.7 +``` + +## Step0: Download ShapeY200 dataset +Run `download.sh` to download the dataset. The script automatically unzips the images under `data/ShapeY200/`. +Downloading uses gdown, which is google drive command line tool. If it does not work, please just follow the two links down below to download the ShapeY200 / ShapeY200CR datasets. + +ShapeY200: +https://drive.google.com/uc?id=1arDu0c9hYLHVMiB52j_a-e0gVnyQfuQV + +ShapeY200CR: +https://drive.google.com/uc?id=1WXpNUVRn6D0F9T3IHruml2DcDCFRsix- + +After downloading the two datasets, move each of them to the `data/` directory. For example, all of the images for ShapeY200 should be under `data/ShapeY200/dataset/`. + +## Step1: Extract the embedding vectors from your own vision model using our dataset +Implement the function `your_feature_output_code` in `step1_save_feature/your_feature_extraction_code.py`. The function should take in the path to the dataset as input and return two lists - one for the image names and another for the corresponding embedding vectors taken from whatever system. + +## Step2: Run macro.py +After you have implemented the function, run `macro.py` to generate the results. +`macro.py` will automatically run the following steps: +1. Compute correlation between all embedding vectors (using `step2_compute_feature_correlation/compute_correlation.py`) + +2. Run benchmark analysis (using `step3_benchmark_analysis/get_nn_classification_error_with_exclusion_distance.py`) + +3. Graph results (top1 object matching error, top1 category matching error, etc.) + +%prep +%autosetup -n ShapeY-0.1.8 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-ShapeY -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.8-1 +- Package Spec generated @@ -0,0 +1 @@ +80204e94c3fb61d40aca29e663b39497 ShapeY-0.1.8.tar.gz |