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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
99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
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import unittest
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import hypothesis.strategies as st
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from hypothesis import given
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import numpy as np
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from caffe2.python import core, workspace
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from caffe2.proto import caffe2_pb2
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import caffe2.python.hypothesis_test_util as hu
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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 TestFallbackOps(hu.HypothesisTestCase):
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@given(stride=st.integers(1, 3),
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pad=st.integers(0, 3),
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kernel=st.integers(3, 5),
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size=st.integers(8, 10),
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input_channels=st.integers(1, 3),
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output_channels=st.integers(1, 5),
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batch_size=st.integers(1, 3),
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use_bias=st.booleans(),
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**mu.gcs)
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def test_in_place(self, stride, pad, kernel, size,
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input_channels, output_channels,
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batch_size, use_bias, gc, dc):
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# To expose fallback in-place potential issue, the fallback op
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# following ideep op must be run at least two iterations.
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conv = core.CreateOperator(
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"Conv",
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["X", "w", "b"] if use_bias else ["X", "w"],
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["Y"],
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stride=stride,
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pad=pad,
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kernel=kernel,
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device_option=dc[0]
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)
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X = np.random.rand(
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batch_size, input_channels, size, size).astype(np.float32) - 0.5
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w = np.random.rand(output_channels, input_channels, kernel, kernel) \
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.astype(np.float32) - 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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old_ws_name = workspace.CurrentWorkspace()
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('X', X, dc[0])
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workspace.FeedBlob('w', w, dc[0])
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workspace.FeedBlob('b', b, dc[0])
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workspace.RunOperatorOnce(conv)
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Y = workspace.FetchBlob('Y')
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scale = np.random.randn(Y.shape[1]).astype(np.float32)
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bias = np.random.randn(Y.shape[1]).astype(np.float32)
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ac = core.CreateOperator(
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"AffineChannel",
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["Y", "scale", "bias"],
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["Y"],
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is_learnable=False,
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device_option=dc[0]
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)
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workspace.FeedBlob('scale', scale, dc[0])
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workspace.FeedBlob('bias', bias, dc[0])
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workspace.RunOperatorOnce(ac)
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workspace.RunOperatorOnce(conv)
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workspace.RunOperatorOnce(ac)
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Y0 = workspace.FetchBlob('Y')
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workspace.ResetWorkspace()
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dev_net = caffe2_pb2.NetDef()
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conv_dev = caffe2_pb2.OperatorDef()
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conv_dev.CopyFrom(conv)
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conv_dev.device_option.CopyFrom(dc[1])
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ac_dev = caffe2_pb2.OperatorDef()
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ac_dev.CopyFrom(ac)
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ac_dev.device_option.CopyFrom(dc[1])
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dev_net.op.extend([conv_dev, ac_dev])
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workspace.FeedBlob('X', X, dc[1])
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workspace.FeedBlob('w', w, dc[1])
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workspace.FeedBlob('b', b, dc[1])
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workspace.FeedBlob('scale', scale, dc[1])
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workspace.FeedBlob('bias', bias, dc[1])
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workspace.RunNetOnce(dev_net)
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workspace.RunNetOnce(dev_net)
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Y1 = workspace.FetchBlob('Y')
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if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01):
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print(Y1.flatten())
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print(Y0.flatten())
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print(np.max(np.abs(Y1 - Y0)))
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self.assertTrue(False)
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workspace.SwitchWorkspace(old_ws_name)
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if __name__ == "__main__":
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unittest.main()
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