Fix LeakyReLU image (#78508)
Fixes #56363, Fixes #78243
| [Before](https://pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html) | [After](https://docs-preview.pytorch.org/78508/generated/torch.nn.LeakyReLU.html) |
| --- | --- |
|  |  |
- Plot `LeakyReLU` with `negative_slope=0.1` instead of `negative_slope=0.01`
- Changed the title from `"{function_name} activation function"` to the name returned by `_get_name()` (with parameter info). The full list is attached at the end.
- Modernized the script and ran black on `docs/source/scripts/build_activation_images.py`. Apologies for the ugly diff.
```
ELU(alpha=1.0)
Hardshrink(0.5)
Hardtanh(min_val=-1.0, max_val=1.0)
Hardsigmoid()
Hardswish()
LeakyReLU(negative_slope=0.1)
LogSigmoid()
PReLU(num_parameters=1)
ReLU()
ReLU6()
RReLU(lower=0.125, upper=0.3333333333333333)
SELU()
SiLU()
Mish()
CELU(alpha=1.0)
GELU(approximate=none)
Sigmoid()
Softplus(beta=1, threshold=20)
Softshrink(0.5)
Softsign()
Tanh()
Tanhshrink()
```
cc @brianjo @mruberry @svekars @holly1238
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78508
Approved by: https://github.com/jbschlosser
diff --git a/docs/source/scripts/build_activation_images.py b/docs/source/scripts/build_activation_images.py
index 3fb4412..94f0416 100644
--- a/docs/source/scripts/build_activation_images.py
+++ b/docs/source/scripts/build_activation_images.py
@@ -4,50 +4,46 @@
online tutorials.
"""
-import os.path
-import torch.nn.modules.activation
-import torch.autograd
+from pathlib import Path
+
+import torch
import matplotlib
+from matplotlib import pyplot as plt
-matplotlib.use('Agg')
-
-import pylab
+matplotlib.use("Agg")
# Create a directory for the images, if it doesn't exist
-ACTIVATION_IMAGE_PATH = os.path.join(
- os.path.realpath(os.path.join(__file__, "..")),
- "activation_images"
-)
+ACTIVATION_IMAGE_PATH = Path(__file__).parent / "activation_images"
-if not os.path.exists(ACTIVATION_IMAGE_PATH):
- os.mkdir(ACTIVATION_IMAGE_PATH)
+if not ACTIVATION_IMAGE_PATH.exists():
+ ACTIVATION_IMAGE_PATH.mkdir()
# In a refactor, these ought to go into their own module or entry
# points so we can generate this list programmaticly
functions = [
- 'ELU',
- 'Hardshrink',
- 'Hardtanh',
- 'Hardsigmoid',
- 'Hardswish',
- 'LeakyReLU', # Perhaps we should add text explaining slight slope?
- 'LogSigmoid',
- 'PReLU',
- 'ReLU',
- 'ReLU6',
- 'RReLU',
- 'SELU',
- 'SiLU',
- 'Mish',
- 'CELU',
- 'GELU',
- 'Sigmoid',
- 'Softplus',
- 'Softshrink',
- 'Softsign',
- 'Tanh',
- 'Tanhshrink'
+ torch.nn.ELU(),
+ torch.nn.Hardshrink(),
+ torch.nn.Hardtanh(),
+ torch.nn.Hardsigmoid(),
+ torch.nn.Hardswish(),
+ torch.nn.LeakyReLU(negative_slope=0.1),
+ torch.nn.LogSigmoid(),
+ torch.nn.PReLU(),
+ torch.nn.ReLU(),
+ torch.nn.ReLU6(),
+ torch.nn.RReLU(),
+ torch.nn.SELU(),
+ torch.nn.SiLU(),
+ torch.nn.Mish(),
+ torch.nn.CELU(),
+ torch.nn.GELU(),
+ torch.nn.Sigmoid(),
+ torch.nn.Softplus(),
+ torch.nn.Softshrink(),
+ torch.nn.Softsign(),
+ torch.nn.Tanh(),
+ torch.nn.Tanhshrink(),
]
@@ -57,33 +53,27 @@
be used to specify color, alpha, etc.
"""
xrange = torch.arange(-7.0, 7.0, 0.01) # We need to go beyond 6 for ReLU6
- pylab.plot(
- xrange.numpy(),
- function(xrange).detach().numpy(),
- **args
- )
+ plt.plot(xrange.numpy(), function(xrange).detach().numpy(), **args)
# Step through all the functions
-for function_name in functions:
- plot_path = os.path.join(ACTIVATION_IMAGE_PATH, function_name + ".png")
- if not os.path.exists(plot_path):
- function = torch.nn.modules.activation.__dict__[function_name]()
-
+for function in functions:
+ function_name = function._get_name()
+ plot_path = ACTIVATION_IMAGE_PATH / f"{function_name}.png"
+ if not plot_path.exists():
# Start a new plot
- pylab.clf()
- pylab.grid(color='k', alpha=0.2, linestyle='--')
+ plt.clf()
+ plt.grid(color="k", alpha=0.2, linestyle="--")
# Plot the current function
plot_function(function)
- # The titles are a little redundant, given context?
- pylab.title(function_name + " activation function")
- pylab.xlabel("Input")
- pylab.ylabel("Output")
- pylab.xlim([-7, 7])
- pylab.ylim([-7, 7])
+ plt.title(function)
+ plt.xlabel("Input")
+ plt.ylabel("Output")
+ plt.xlim([-7, 7])
+ plt.ylim([-7, 7])
# And save it
- pylab.savefig(plot_path)
- print('Saved activation image for {} at {}'.format(function, plot_path))
+ plt.savefig(plot_path)
+ print(f"Saved activation image for {function_name} at {plot_path}")
