%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
![Test](https://github.com/yueyericardo/pkbar/workflows/Test/badge.svg) [![PyPI version](https://badge.fury.io/py/pkbar.svg)](https://badge.fury.io/py/pkbar) [![pypidownload](https://img.shields.io/pypi/dm/pkbar.svg)](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
![Test](https://github.com/yueyericardo/pkbar/workflows/Test/badge.svg) [![PyPI version](https://badge.fury.io/py/pkbar.svg)](https://badge.fury.io/py/pkbar) [![pypidownload](https://img.shields.io/pypi/dm/pkbar.svg)](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
![Test](https://github.com/yueyericardo/pkbar/workflows/Test/badge.svg) [![PyPI version](https://badge.fury.io/py/pkbar.svg)](https://badge.fury.io/py/pkbar) [![pypidownload](https://img.shields.io/pypi/dm/pkbar.svg)](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 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5-1
- Package Spec generated