1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
|
%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.aliyun.com/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
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 2.2.5-1
- Package Spec generated
|