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243dd7e74f
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
80 lines
2.1 KiB
Python
80 lines
2.1 KiB
Python
"""
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This script will generate input-out plots for all of the activation
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functions. These are for use in the documentation, and potentially in
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online tutorials.
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"""
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from pathlib import Path
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import torch
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import matplotlib
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from matplotlib import pyplot as plt
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matplotlib.use("Agg")
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# Create a directory for the images, if it doesn't exist
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ACTIVATION_IMAGE_PATH = Path(__file__).parent / "activation_images"
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if not ACTIVATION_IMAGE_PATH.exists():
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ACTIVATION_IMAGE_PATH.mkdir()
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# In a refactor, these ought to go into their own module or entry
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# points so we can generate this list programmaticly
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functions = [
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torch.nn.ELU(),
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torch.nn.Hardshrink(),
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torch.nn.Hardtanh(),
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torch.nn.Hardsigmoid(),
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torch.nn.Hardswish(),
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torch.nn.LeakyReLU(negative_slope=0.1),
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torch.nn.LogSigmoid(),
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torch.nn.PReLU(),
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torch.nn.ReLU(),
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torch.nn.ReLU6(),
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torch.nn.RReLU(),
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torch.nn.SELU(),
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torch.nn.SiLU(),
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torch.nn.Mish(),
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torch.nn.CELU(),
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torch.nn.GELU(),
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torch.nn.Sigmoid(),
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torch.nn.Softplus(),
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torch.nn.Softshrink(),
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torch.nn.Softsign(),
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torch.nn.Tanh(),
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torch.nn.Tanhshrink(),
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]
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def plot_function(function, **args):
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"""
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Plot a function on the current plot. The additional arguments may
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be used to specify color, alpha, etc.
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"""
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xrange = torch.arange(-7.0, 7.0, 0.01) # We need to go beyond 6 for ReLU6
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plt.plot(xrange.numpy(), function(xrange).detach().numpy(), **args)
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# Step through all the functions
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for function in functions:
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function_name = function._get_name()
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plot_path = ACTIVATION_IMAGE_PATH / f"{function_name}.png"
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if not plot_path.exists():
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# Start a new plot
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plt.clf()
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plt.grid(color="k", alpha=0.2, linestyle="--")
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# Plot the current function
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plot_function(function)
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plt.title(function)
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plt.xlabel("Input")
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plt.ylabel("Output")
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plt.xlim([-7, 7])
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plt.ylim([-7, 7])
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# And save it
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plt.savefig(plot_path)
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print(f"Saved activation image for {function_name} at {plot_path}")
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