mirror of
https://github.com/saymrwulf/pytorch.git
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8fab453863
Summary: Due to popular demand, added an op to compute element-wise square + gradient for it (just for the fun of it). Reviewed By: Yangqing Differential Revision: D4664797 fbshipit-source-id: 0a29c7c249fdc72f51412bebd6ae352a7801cf05
84 lines
2.1 KiB
Python
84 lines
2.1 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core
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from hypothesis import given
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import caffe2.python.hypothesis_test_util as hu
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import hypothesis.strategies as st
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import numpy as np
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class TestElementwiseOps(hu.HypothesisTestCase):
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@given(n=st.integers(2, 10), m=st.integers(4, 6),
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d=st.integers(2, 3), **hu.gcs)
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def test_div(self, n, m, d, gc, dc):
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X = np.random.rand(n, m, d).astype(np.float32)
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Y = np.random.rand(n, m, d).astype(np.float32) + 5.0
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def div_op(X, Y):
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return [np.divide(X, Y)]
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op = core.CreateOperator(
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"Div",
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["X", "Y"],
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["Z"]
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X, Y],
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reference=div_op,
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)
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self.assertGradientChecks(
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gc, op, [X, Y], 0, [0], stepsize=1e-4, threshold=1e-2)
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@given(n=st.integers(5, 6), m=st.integers(4, 6), **hu.gcs)
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def test_log(self, n, m, gc, dc):
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X = np.random.rand(n, m).astype(np.float32) + 1.0
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def log_op(X):
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return [np.log(X)]
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op = core.CreateOperator(
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"Log",
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["X"],
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["Z"]
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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reference=log_op,
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)
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self.assertGradientChecks(
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gc, op, [X], 0, [0], stepsize=1e-4, threshold=1e-2)
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@given(n=st.integers(5, 6), m=st.integers(4, 6), **hu.gcs)
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def test_sqr(self, n, m, gc, dc):
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X = np.random.rand(n, m).astype(np.float32)
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def sqr_op(X):
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return [np.square(X)]
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op = core.CreateOperator(
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"Sqr",
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["X"],
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["Z"]
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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reference=sqr_op,
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)
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self.assertGradientChecks(
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gc, op, [X], 0, [0], stepsize=1e-4, threshold=1e-2)
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