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|
%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
|
