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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
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
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from caffe2.python import core, workspace
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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 TestScaleOps(serial.SerializedTestCase):
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@serial.given(dim=st.sampled_from([[1, 386, 1], [386, 1, 1],
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[1, 256, 1], [256, 1, 1],
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[1024, 256, 1], [1, 1024, 1],
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[1, 1, 1]]),
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scale=st.floats(0.0, 10.0),
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num_tensors=st.integers(1, 10),
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**hu.gcs)
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def test_scale_ops(self, dim, scale, num_tensors, gc, dc):
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in_tensors = []
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in_tensor_ps = []
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out_tensors = []
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out_ref_tensors = []
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# initialize tensors
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for i in range(num_tensors):
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tensor = "X_{}".format(i)
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X = np.random.rand(*dim).astype(np.float32) - 0.5
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in_tensors.append(tensor)
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in_tensor_ps.append(X)
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out_tensor = "O_{}".format(i)
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out_tensors.append(out_tensor)
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workspace.FeedBlob(tensor, X, device_option=gc)
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# run ScaleBlobs operator
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scale_blobs_op = core.CreateOperator(
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"ScaleBlobs",
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in_tensors,
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out_tensors,
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scale=scale,
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)
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scale_blobs_op.device_option.CopyFrom(gc)
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workspace.RunOperatorOnce(scale_blobs_op)
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# run Scale op for each tensor and compare with ScaleBlobs
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for i in range(num_tensors):
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tensor = "X_{}".format(i)
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out_ref_tensor = "O_ref_{}".format(i)
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scale_op = core.CreateOperator(
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"Scale",
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[tensor],
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[out_ref_tensor],
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scale=scale,
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)
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scale_op.device_option.CopyFrom(gc)
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workspace.RunOperatorOnce(scale_op)
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o_ref = workspace.FetchBlob(out_ref_tensor)
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o = workspace.FetchBlob(out_tensors[i])
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np.testing.assert_allclose(o, o_ref)
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if __name__ == '__main__':
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unittest.main()
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