mirror of
https://github.com/zebrajr/pytorch.git
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2863d935b9
* Sketch fix of the performance issue of IDEEP * Revert CMakefile * Fix tests * format * comments * Print error * review comments
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import unittest
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import hypothesis.strategies as st
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from hypothesis import given, settings, unlimited
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import numpy as np
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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.ideep_test_util as mu
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@unittest.skipIf(not workspace.C.use_ideep, "No IDEEP support.")
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class ConvTest(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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training_mode=st.booleans(),
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group=st.integers(1, 2),
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**mu.gcs)
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@settings(deadline=None, timeout=unlimited)
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def test_convolution(self, stride, pad, kernel, size,
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input_channels, output_channels,
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batch_size, use_bias, training_mode, group, gc, dc):
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training = 1 if training_mode else 0
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op = 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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group=group,
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training_mode=training,
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)
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X = np.random.rand(
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batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels * group, input_channels, kernel, kernel) \
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.astype(np.float32) - 0.5
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b = np.random.rand(output_channels * group).astype(np.float32) - 0.5
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inputs = [X, w, b] if use_bias else [X, w]
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self.assertDeviceChecks(dc, op, inputs, [0])
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if training_mode:
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for i in range(len(inputs)):
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self.assertGradientChecks(gc, op, inputs, i, [0], threshold=0.01)
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if __name__ == "__main__":
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unittest.main()
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