Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/yueyericardo/pkbar
Keras style progressbar for Pytorch (PK Bar)
https://github.com/yueyericardo/pkbar
keras progress-bar pytorch
Last synced: 6 days ago
JSON representation
Keras style progressbar for Pytorch (PK Bar)
- Host: GitHub
- URL: https://github.com/yueyericardo/pkbar
- Owner: yueyericardo
- License: apache-2.0
- Created: 2019-08-09T16:36:30.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-15T21:27:41.000Z (6 months ago)
- Last Synced: 2024-10-09T12:33:07.973Z (28 days ago)
- Topics: keras, progress-bar, pytorch
- Language: Python
- Homepage: https://pypi.org/project/pkbar/
- Size: 38.1 KB
- Stars: 32
- Watchers: 3
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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)