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173f224570
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598 ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a Stack from [ghstack](https://github.com/ezyang/ghstack): * **#18598 Turn on F401: Unused import warning.** This was requested by someone at Facebook; this lint is turned on for Facebook by default. "Sure, why not." I had to noqa a number of imports in __init__. Hypothetically we're supposed to use __all__ in this case, but I was too lazy to fix it. Left for future work. Be careful! flake8-2 and flake8-3 behave differently with respect to import resolution for # type: comments. flake8-3 will report an import unused; flake8-2 will not. For now, I just noqa'd all these sites. All the changes were done by hand. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: D14687478 fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
import torch
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import torch.jit
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import numpy as np
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import unittest
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from caffe2.python import core
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from common_utils import TestCase, run_tests
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def canonical(graph):
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return str(torch._C._jit_pass_canonicalize(graph))
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@unittest.skipIf("Relu_ENGINE_DNNLOWP" not in core._REGISTERED_OPERATORS, "fbgemm-based Caffe2 ops are not linked")
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class TestQuantized(TestCase):
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def test_relu(self):
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a = (torch.tensor([4, 6, 1, 10], dtype=torch.uint8), 0.01, 5)
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r = torch.ops.c10.quantized_relu(a)
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np.testing.assert_equal(r[0].numpy(), torch.tensor([5, 6, 5, 10], dtype=torch.uint8).numpy())
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np.testing.assert_almost_equal(0.01, r[1])
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self.assertEqual(5, r[2])
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def test_quantize(self):
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a = (torch.tensor([4, 6, 1, 10], dtype=torch.uint8), 0.01, 5)
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r = torch.ops.c10.dequantize(a)
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np.testing.assert_almost_equal(r.numpy(), [-0.01, 0.01, -0.04, 0.05])
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# default args
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q_def = torch.ops.c10.quantize(r)
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# specified
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q = torch.ops.c10.quantize(r, scale=0.01, zero_point=5)
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np.testing.assert_equal(q[0].numpy(), a[0].numpy())
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np.testing.assert_almost_equal(q[1], a[1])
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self.assertEqual(q[2], a[2])
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def test_script(self):
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@torch.jit.script
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def foo(x):
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# type: (Tuple[Tensor, float, int]) -> Tuple[Tensor, float, int]
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return torch.ops.c10.quantized_relu(x)
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self.assertExpectedInline(canonical(foo.graph), '''\
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graph(%x : (Tensor, float, int)):
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%1 : (Tensor, float, int) = c10::quantized_relu(%x)
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return (%1)
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''')
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if __name__ == '__main__':
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run_tests()
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