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https://github.com/saymrwulf/pytorch.git
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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
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import numpy as np
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import unittest
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from caffe2.python import core, workspace, tt_core
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import caffe2.python.hypothesis_test_util as hu
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class TestTTSVD(hu.HypothesisTestCase):
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def test_full_tt_svd(self):
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size = 256
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np.random.seed(1234)
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X = np.expand_dims(
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np.random.rand(size).astype(np.float32), axis=0)
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W = np.random.rand(size, size).astype(np.float32)
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b = np.zeros(size).astype(np.float32)
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inp_sizes = [4, 4, 4, 4]
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out_sizes = [4, 4, 4, 4]
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op_fc = core.CreateOperator(
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"FC",
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["X", "W", "b"],
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["Y"],
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)
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("W", W)
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workspace.FeedBlob("b", b)
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workspace.RunOperatorOnce(op_fc)
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Y_fc = workspace.FetchBlob("Y").flatten()
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# Testing TT-decomposition with high ranks
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full_tt_ranks = [1, 16, 256, 16, 1]
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full_cores = tt_core.matrix_to_tt(W, inp_sizes, out_sizes,
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full_tt_ranks)
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full_op_tt = core.CreateOperator(
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"TT",
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["X", "b", "cores"],
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["Y"],
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inp_sizes=inp_sizes,
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out_sizes=out_sizes,
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tt_ranks=full_tt_ranks,
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)
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("b", b)
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workspace.FeedBlob("cores", full_cores)
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workspace.RunOperatorOnce(full_op_tt)
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Y_full_tt = workspace.FetchBlob("Y").flatten()
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assert(len(Y_fc) == len(Y_full_tt))
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self.assertAlmostEquals(np.linalg.norm(Y_fc - Y_full_tt), 0, delta=1e-3)
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# Testing TT-decomposition with minimal ranks
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sparse_tt_ranks = [1, 1, 1, 1, 1]
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sparse_cores = tt_core.matrix_to_tt(W, inp_sizes, out_sizes,
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sparse_tt_ranks)
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sparse_op_tt = core.CreateOperator(
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"TT",
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["X", "b", "cores"],
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["Y"],
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inp_sizes=inp_sizes,
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out_sizes=out_sizes,
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tt_ranks=sparse_tt_ranks,
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)
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("b", b)
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workspace.FeedBlob("cores", sparse_cores)
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workspace.RunOperatorOnce(sparse_op_tt)
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Y_sparse_tt = workspace.FetchBlob("Y").flatten()
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assert(len(Y_fc) == len(Y_sparse_tt))
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self.assertAlmostEquals(np.linalg.norm(Y_fc - Y_sparse_tt),
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39.974, delta=1e-3)
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if __name__ == '__main__':
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unittest.main()
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