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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
83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
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import numpy as np
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import hypothesis.strategies as st
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import unittest
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import caffe2.python.hypothesis_test_util as hu
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from caffe2.python import core, workspace
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from hypothesis import given
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import caffe2.python.ideep_test_util as mu
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class TestAdamOps(hu.HypothesisTestCase):
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@given(inputs=hu.tensors(n=4),
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ITER=st.integers(min_value=0, max_value=10000),
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LR=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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beta1=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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beta2=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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**mu.gcs)
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def test_adam(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc):
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param, mom1, mom2, grad = inputs
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ITER = np.array([ITER], dtype=np.int64)
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LR = np.array([LR], dtype=np.float32)
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mom2 = np.absolute(mom2)
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op = core.CreateOperator(
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"Adam",
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["param", "mom1", "mom2", "grad", "lr", "iter"],
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["output_param", "output_mom1", "output_mom2"],
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beta1=beta1, beta2=beta2, epsilon=epsilon)
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# Iter lives on the CPU
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input_device_options = {'iter': hu.cpu_do, 'lr': hu.cpu_do}
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self.assertDeviceChecks(
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dc, op,
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[param, mom1, mom2, grad, LR, ITER],
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[0],
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input_device_options=input_device_options,
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threshold=0.001)
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@given(inputs=hu.tensors(n=4),
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ITER=st.integers(min_value=0, max_value=10000),
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LR=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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beta1=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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beta2=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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epsilon=st.floats(min_value=0.01, max_value=0.99,
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allow_nan=False, allow_infinity=False),
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**mu.gcs)
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def test_adam_output_grad(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc):
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param, mom1, mom2, grad = inputs
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ITER = np.array([ITER], dtype=np.int64)
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LR = np.array([LR], dtype=np.float32)
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mom2 = np.absolute(mom2)
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op = core.CreateOperator(
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"Adam",
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["param", "mom1", "mom2", "grad", "lr", "iter"],
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["output_param", "output_mom1", "output_mom2", "output_grad"],
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beta1=beta1, beta2=beta2, epsilon=epsilon)
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# Iter lives on the CPU
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input_device_options = {'iter': hu.cpu_do, 'lr': hu.cpu_do}
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self.assertDeviceChecks(
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dc, op,
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[param, mom1, mom2, grad, LR, ITER],
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[0],
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input_device_options=input_device_options,
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threshold=0.001)
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if __name__ == "__main__":
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unittest.main()
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