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https://github.com/saymrwulf/pytorch.git
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9945fd7253
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49980 From ``` ./python/libcst/libcst codemod remove_unused_imports.RemoveUnusedImportsWithGlean --no-format caffe2/ ``` Test Plan: Standard sandcastle tests Reviewed By: xush6528 Differential Revision: D25727359 fbshipit-source-id: c4f60005b10546423dc093d31d46deb418352286
108 lines
4.2 KiB
Python
108 lines
4.2 KiB
Python
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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.serialized_test.serialized_test_util as serial
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import hypothesis.strategies as st
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import numpy as np
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class DistanceTest(serial.SerializedTestCase):
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@serial.given(n=st.integers(1, 3),
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dim=st.integers(4, 16),
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**hu.gcs)
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def test_cosine_similarity(self, n, dim, gc, dc):
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X = np.random.uniform(-1, 1, (n, dim)).astype(np.float32)
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Y = np.random.uniform(-1, 1, (n, dim)).astype(np.float32)
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self.ws.create_blob("X").feed(X)
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self.ws.create_blob("Y").feed(Y)
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kEps = 1e-12
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cos_op = core.CreateOperator("CosineSimilarity", ["X", "Y"], ["cos"])
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self.ws.run(cos_op)
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cos = np.divide(np.multiply(X, Y).sum(axis=1),
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np.multiply(np.linalg.norm(X, axis=1) + kEps,
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np.linalg.norm(Y, axis=1) + kEps))
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np.testing.assert_allclose(self.ws.blobs[("cos")].fetch(), cos,
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rtol=1e-4, atol=1e-4)
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self.assertGradientChecks(gc, cos_op, [X, Y], 0, [0],
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stepsize=1e-2, threshold=1e-2)
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self.assertGradientChecks(gc, cos_op, [X, Y], 1, [0],
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stepsize=1e-2, threshold=1e-2)
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@serial.given(inputs=hu.tensors(n=2,
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min_dim=1,
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max_dim=2,
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dtype=np.float32),
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**hu.gcs)
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def test_dot_product(self, inputs, gc, dc):
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X, Y = inputs
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op = core.CreateOperator(
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'DotProduct',
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['X', 'Y'],
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['DOT'],
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)
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def dot_ref(X, Y):
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return ([np.dot(x, y) for x, y in zip(X, Y)],)
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# Check against numpy dot reference
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self.assertReferenceChecks(gc, op, [X, Y], dot_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X, Y], [0])
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# Gradient check wrt X
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self.assertGradientChecks(gc, op, [X, Y], 0, [0])
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# Gradient check wrt Y
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self.assertGradientChecks(gc, op, [X, Y], 1, [0])
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@serial.given(n=st.integers(1, 3),
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dim=st.integers(4, 16),
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**hu.gcs)
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def test_L1_distance(self, n, dim, gc, dc):
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X = np.random.uniform(-1, 1, (n, dim)).astype(np.float32)
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Y = np.random.uniform(-1, 1, (n, dim)).astype(np.float32)
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# avoid kinks by moving away from 0
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X += 0.02 * np.sign(X - Y)
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X[(X - Y) == 0.0] += 0.02
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self.ws.create_blob("X").feed(X)
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self.ws.create_blob("Y").feed(Y)
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op = core.CreateOperator(
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'L1Distance',
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['X', 'Y'],
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['l1_dist'],
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)
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self.ws.run(op)
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np.testing.assert_allclose(self.ws.blobs[("l1_dist")].fetch(),
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[np.linalg.norm(x - y, ord=1)
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for x, y in zip(X, Y)],
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rtol=1e-4, atol=1e-4)
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self.assertDeviceChecks(dc, op, [X, Y], [0])
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# Gradient check wrt X
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self.assertGradientChecks(gc, op, [X, Y], 0, [0],
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stepsize=1e-2, threshold=1e-2)
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# Gradient check wrt Y
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self.assertGradientChecks(gc, op, [X, Y], 1, [0],
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stepsize=1e-2, threshold=1e-2)
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@serial.given(n=st.integers(1, 3),
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dim=st.integers(4, 16),
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**hu.gcs)
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def test_L2_distance(self, n, dim, gc, dc):
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X = np.random.uniform(-1, 1, (n, dim)).astype(np.float32)
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Y = np.random.uniform(-1, 1, (n, dim)).astype(np.float32)
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self.ws.create_blob("X").feed(X)
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self.ws.create_blob("Y").feed(Y)
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l2_op = core.CreateOperator("SquaredL2Distance",
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["X", "Y"], ["l2_dist"])
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self.ws.run(l2_op)
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np.testing.assert_allclose(self.ws.blobs[("l2_dist")].fetch(),
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np.square(X - Y).sum(axis=1) * 0.5,
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rtol=1e-4, atol=1e-4)
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self.assertGradientChecks(gc, l2_op, [X, Y], 0, [0],
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stepsize=1e-2, threshold=1e-2)
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self.assertGradientChecks(gc, l2_op, [X, Y], 1, [0],
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stepsize=1e-2, threshold=1e-2)
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