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|
%global _empty_manifest_terminate_build 0
Name: python-pkbar
Version: 0.5
Release: 1
Summary: Keras Progress Bar for PyTorch
License: Apache License 2.0
URL: https://github.com/yueyericardo/pkbar
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/12/4d/c4210a0743ef62ddfa96b3b501c71a214718189f65df8a22f1eb37f256e3/pkbar-0.5.tar.gz
BuildArch: noarch
Requires: python3-numpy
%description
# pkbar
 [](https://badge.fury.io/py/pkbar) [](https://pypistats.org/packages/pkbar)
Keras style progressbar for pytorch (PK Bar)
### 1. Show
- `pkbar.Pbar` (progress bar)
```
loading and processing dataset
10/10 [==============================] - 1.0s
```
- `pkbar.Kbar` (keras bar)
```
Epoch: 1/3
100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269
Epoch: 2/3
100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261
Epoch: 3/3
100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.4254
```
### 2. Install
```
pip install pkbar
```
### 3. Usage
- `pkbar.Pbar` (progress bar)
```python
import pkbar
import time
pbar = pkbar.Pbar(name='loading and processing dataset', target=10)
for i in range(10):
time.sleep(0.1)
pbar.update(i)
```
```
loading and processing dataset
10/10 [==============================] - 1.0s
```
- `pkbar.Kbar` (keras bar) [for a concreate example](https://github.com/yueyericardo/pkbar/blob/master/tests/test.py#L16)
```python
import pkbar
import torch
# training loop
train_per_epoch = num_of_batches_per_epoch
for epoch in range(num_epochs):
################################### Initialization ########################################
kbar = pkbar.Kbar(target=train_per_epoch, epoch=epoch, num_epochs=num_epochs, width=8, always_stateful=False)
# By default, all metrics are averaged over time. If you don't want this behavior, you could either:
# 1. Set always_stateful to True, or
# 2. Set stateful_metrics=["loss", "rmse", "val_loss", "val_rmse"], Metrics in this list will be displayed as-is.
# All others will be averaged by the progbar before display.
###########################################################################################
# training
for i in range(train_per_epoch):
outputs = model(inputs)
train_loss = criterion(outputs, targets)
train_rmse = torch.sqrt(train_loss)
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
############################# Update after each batch ##################################
kbar.update(i, values=[("loss", train_loss), ("rmse", train_rmse)])
########################################################################################
# validation
outputs = model(inputs)
val_loss = criterion(outputs, targets)
val_rmse = torch.sqrt(val_loss)
################################ Add validation metrics ###################################
kbar.add(1, values=[("val_loss", val_loss), ("val_rmse", val_rmse)])
###########################################################################################
```
```
Epoch: 1/3
100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269
Epoch: 2/3
100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261
Epoch: 3/3
100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.4254
```
### 4. Acknowledge
Keras progbar's code from [`tf.keras.utils.Progbar`](https://github.com/tensorflow/tensorflow/blob/r1.14/tensorflow/python/keras/utils/generic_utils.py#L313)
%package -n python3-pkbar
Summary: Keras Progress Bar for PyTorch
Provides: python-pkbar
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pkbar
# pkbar
 [](https://badge.fury.io/py/pkbar) [](https://pypistats.org/packages/pkbar)
Keras style progressbar for pytorch (PK Bar)
### 1. Show
- `pkbar.Pbar` (progress bar)
```
loading and processing dataset
10/10 [==============================] - 1.0s
```
- `pkbar.Kbar` (keras bar)
```
Epoch: 1/3
100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269
Epoch: 2/3
100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261
Epoch: 3/3
100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.4254
```
### 2. Install
```
pip install pkbar
```
### 3. Usage
- `pkbar.Pbar` (progress bar)
```python
import pkbar
import time
pbar = pkbar.Pbar(name='loading and processing dataset', target=10)
for i in range(10):
time.sleep(0.1)
pbar.update(i)
```
```
loading and processing dataset
10/10 [==============================] - 1.0s
```
- `pkbar.Kbar` (keras bar) [for a concreate example](https://github.com/yueyericardo/pkbar/blob/master/tests/test.py#L16)
```python
import pkbar
import torch
# training loop
train_per_epoch = num_of_batches_per_epoch
for epoch in range(num_epochs):
################################### Initialization ########################################
kbar = pkbar.Kbar(target=train_per_epoch, epoch=epoch, num_epochs=num_epochs, width=8, always_stateful=False)
# By default, all metrics are averaged over time. If you don't want this behavior, you could either:
# 1. Set always_stateful to True, or
# 2. Set stateful_metrics=["loss", "rmse", "val_loss", "val_rmse"], Metrics in this list will be displayed as-is.
# All others will be averaged by the progbar before display.
###########################################################################################
# training
for i in range(train_per_epoch):
outputs = model(inputs)
train_loss = criterion(outputs, targets)
train_rmse = torch.sqrt(train_loss)
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
############################# Update after each batch ##################################
kbar.update(i, values=[("loss", train_loss), ("rmse", train_rmse)])
########################################################################################
# validation
outputs = model(inputs)
val_loss = criterion(outputs, targets)
val_rmse = torch.sqrt(val_loss)
################################ Add validation metrics ###################################
kbar.add(1, values=[("val_loss", val_loss), ("val_rmse", val_rmse)])
###########################################################################################
```
```
Epoch: 1/3
100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269
Epoch: 2/3
100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261
Epoch: 3/3
100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.4254
```
### 4. Acknowledge
Keras progbar's code from [`tf.keras.utils.Progbar`](https://github.com/tensorflow/tensorflow/blob/r1.14/tensorflow/python/keras/utils/generic_utils.py#L313)
%package help
Summary: Development documents and examples for pkbar
Provides: python3-pkbar-doc
%description help
# pkbar
 [](https://badge.fury.io/py/pkbar) [](https://pypistats.org/packages/pkbar)
Keras style progressbar for pytorch (PK Bar)
### 1. Show
- `pkbar.Pbar` (progress bar)
```
loading and processing dataset
10/10 [==============================] - 1.0s
```
- `pkbar.Kbar` (keras bar)
```
Epoch: 1/3
100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269
Epoch: 2/3
100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261
Epoch: 3/3
100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.4254
```
### 2. Install
```
pip install pkbar
```
### 3. Usage
- `pkbar.Pbar` (progress bar)
```python
import pkbar
import time
pbar = pkbar.Pbar(name='loading and processing dataset', target=10)
for i in range(10):
time.sleep(0.1)
pbar.update(i)
```
```
loading and processing dataset
10/10 [==============================] - 1.0s
```
- `pkbar.Kbar` (keras bar) [for a concreate example](https://github.com/yueyericardo/pkbar/blob/master/tests/test.py#L16)
```python
import pkbar
import torch
# training loop
train_per_epoch = num_of_batches_per_epoch
for epoch in range(num_epochs):
################################### Initialization ########################################
kbar = pkbar.Kbar(target=train_per_epoch, epoch=epoch, num_epochs=num_epochs, width=8, always_stateful=False)
# By default, all metrics are averaged over time. If you don't want this behavior, you could either:
# 1. Set always_stateful to True, or
# 2. Set stateful_metrics=["loss", "rmse", "val_loss", "val_rmse"], Metrics in this list will be displayed as-is.
# All others will be averaged by the progbar before display.
###########################################################################################
# training
for i in range(train_per_epoch):
outputs = model(inputs)
train_loss = criterion(outputs, targets)
train_rmse = torch.sqrt(train_loss)
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
############################# Update after each batch ##################################
kbar.update(i, values=[("loss", train_loss), ("rmse", train_rmse)])
########################################################################################
# validation
outputs = model(inputs)
val_loss = criterion(outputs, targets)
val_rmse = torch.sqrt(val_loss)
################################ Add validation metrics ###################################
kbar.add(1, values=[("val_loss", val_loss), ("val_rmse", val_rmse)])
###########################################################################################
```
```
Epoch: 1/3
100/100 [========] - 10s 102ms/step - loss: 3.7782 - rmse: 1.1650 - val_loss: 0.1823 - val_rmse: 0.4269
Epoch: 2/3
100/100 [========] - 10s 101ms/step - loss: 0.1819 - rmse: 0.4265 - val_loss: 0.1816 - val_rmse: 0.4261
Epoch: 3/3
100/100 [========] - 10s 101ms/step - loss: 0.1813 - rmse: 0.4258 - val_loss: 0.1810 - val_rmse: 0.4254
```
### 4. Acknowledge
Keras progbar's code from [`tf.keras.utils.Progbar`](https://github.com/tensorflow/tensorflow/blob/r1.14/tensorflow/python/keras/utils/generic_utils.py#L313)
%prep
%autosetup -n pkbar-0.5
%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-pkbar -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5-1
- Package Spec generated
|
