From 35c013eeea0e102d63be4b04464c46940ab6f1e8 Mon Sep 17 00:00:00 2001
From: CoprDistGit <infra@openeuler.org>
Date: Mon, 29 May 2023 10:54:31 +0000
Subject: automatic import of python-dnnlab

---
 python-dnnlab.spec | 211 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 211 insertions(+)
 create mode 100644 python-dnnlab.spec

(limited to 'python-dnnlab.spec')

diff --git a/python-dnnlab.spec b/python-dnnlab.spec
new file mode 100644
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+++ b/python-dnnlab.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-dnnlab
+Version:	2.2.5
+Release:	1
+Summary:	DnnLab
+License:	Apache Software License
+URL:		https://pypi.org/project/dnnlab/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/4e/05/13de5b2635ea6158bcc084e433ac34cb5ea1e7a6c523e13b5747217137d7/dnnlab-2.2.5.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-Cython
+Requires:	python3-numpy
+Requires:	python3-pycocotools
+Requires:	python3-Click
+Requires:	python3-opencv-python
+Requires:	python3-imgaug
+Requires:	python3-matplotlib
+
+%description
+# DnnLab
+Dnnlab is a small framework for deep learning models based on TensorFlow.
+
+
+
+It provides custom training loops for:
+* Generative Models (GAN, cGan, cycleGAN)
+* Image Detection (custom YOLO)
+
+
+Additonaly custom Keras Layer:
+* Non-Local-Blocks (Self-Attention)
+* Squeeze and Excitation Blocks (SEBlocks)
+* YOLO-Decoding Layer
+
+Input pipeline functionality:
+* YOLO (Tfrecords to Datasets)
+* YOLO data augmentation
+* Generative Models (Tfrecords to Datasets)
+
+TensorBoard output:
+* YOLO coco metrics (Precision (mAP) & Recall)
+* YOLO loss (loss_class, loss_conf, loss_xywh, total_loss)
+* YOLO bounding boxes
+* Generative Models (Loss & Images)
+
+
+## Requirements
+TensorFlow 2.3.0
+
+## Installation
+Run the following to install:
+```python
+pip install dnnlab
+```
+
+
+
+
+
+
+
+
+
+
+
+%package -n python3-dnnlab
+Summary:	DnnLab
+Provides:	python-dnnlab
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-dnnlab
+# DnnLab
+Dnnlab is a small framework for deep learning models based on TensorFlow.
+
+
+
+It provides custom training loops for:
+* Generative Models (GAN, cGan, cycleGAN)
+* Image Detection (custom YOLO)
+
+
+Additonaly custom Keras Layer:
+* Non-Local-Blocks (Self-Attention)
+* Squeeze and Excitation Blocks (SEBlocks)
+* YOLO-Decoding Layer
+
+Input pipeline functionality:
+* YOLO (Tfrecords to Datasets)
+* YOLO data augmentation
+* Generative Models (Tfrecords to Datasets)
+
+TensorBoard output:
+* YOLO coco metrics (Precision (mAP) & Recall)
+* YOLO loss (loss_class, loss_conf, loss_xywh, total_loss)
+* YOLO bounding boxes
+* Generative Models (Loss & Images)
+
+
+## Requirements
+TensorFlow 2.3.0
+
+## Installation
+Run the following to install:
+```python
+pip install dnnlab
+```
+
+
+
+
+
+
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for dnnlab
+Provides:	python3-dnnlab-doc
+%description help
+# DnnLab
+Dnnlab is a small framework for deep learning models based on TensorFlow.
+
+
+
+It provides custom training loops for:
+* Generative Models (GAN, cGan, cycleGAN)
+* Image Detection (custom YOLO)
+
+
+Additonaly custom Keras Layer:
+* Non-Local-Blocks (Self-Attention)
+* Squeeze and Excitation Blocks (SEBlocks)
+* YOLO-Decoding Layer
+
+Input pipeline functionality:
+* YOLO (Tfrecords to Datasets)
+* YOLO data augmentation
+* Generative Models (Tfrecords to Datasets)
+
+TensorBoard output:
+* YOLO coco metrics (Precision (mAP) & Recall)
+* YOLO loss (loss_class, loss_conf, loss_xywh, total_loss)
+* YOLO bounding boxes
+* Generative Models (Loss & Images)
+
+
+## Requirements
+TensorFlow 2.3.0
+
+## Installation
+Run the following to install:
+```python
+pip install dnnlab
+```
+
+
+
+
+
+
+
+
+
+
+
+%prep
+%autosetup -n dnnlab-2.2.5
+
+%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-dnnlab -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 2.2.5-1
+- Package Spec generated
-- 
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