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b004307252
Test Plan: manual inspection & sandcastle Reviewed By: zertosh Differential Revision: D30279364 fbshipit-source-id: c1ed77dfe43a3bde358f92737cd5535ae5d13c9a
87 lines
2.2 KiB
Python
87 lines
2.2 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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import os.path
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import matplotlib
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import torch.autograd
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import torch.nn.modules.activation
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matplotlib.use("Agg")
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import pylab
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# Create a directory for the images, if it doesn't exist
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ACTIVATION_IMAGE_PATH = os.path.join(
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os.path.realpath(os.path.join(__file__, "..")), "activation_images"
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)
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if not os.path.exists(ACTIVATION_IMAGE_PATH):
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os.mkdir(ACTIVATION_IMAGE_PATH)
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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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"ELU",
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"Hardshrink",
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"Hardtanh",
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"LeakyReLU", # Perhaps we should add text explaining slight slope?
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"LogSigmoid",
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"PReLU",
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"ReLU",
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"ReLU6",
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"RReLU",
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"SELU",
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"SiLU",
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"Mish",
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"CELU",
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"GELU",
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"Sigmoid",
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"Softplus",
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"Softshrink",
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"Softsign",
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"Tanh",
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"Tanhshrink"
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# 'Threshold' Omit, pending cleanup. See PR5457
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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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pylab.plot(
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xrange.numpy(), function(torch.autograd.Variable(xrange)).data.numpy(), **args
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)
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# Step through all the functions
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for function_name in functions:
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plot_path = os.path.join(ACTIVATION_IMAGE_PATH, function_name + ".png")
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if not os.path.exists(plot_path):
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function = torch.nn.modules.activation.__dict__[function_name]()
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# Start a new plot
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pylab.clf()
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pylab.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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# The titles are a little redundant, given context?
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pylab.title(function_name + " activation function")
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pylab.xlabel("Input")
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pylab.ylabel("Output")
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pylab.xlim([-7, 7])
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pylab.ylim([-7, 7])
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# And save it
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pylab.savefig(plot_path)
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print("Saved activation image for {} at {}".format(function, plot_path))
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