mirror of
https://github.com/saymrwulf/pytorch.git
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2c6a579859
Summary: It used to be that only the cudnn engine supports it, and now it should be fully supported by any conv engine. To ignore bias, simply use a convolution op that has two inputs instead of 3. The gradient operator will automatically figure out that it does not compute the bias gradient. Reviewed By: prigoyal Differential Revision: D4354183 fbshipit-source-id: cf71b6289a254d15a6a663a85df63fbbaec3702b
71 lines
2.4 KiB
Python
71 lines
2.4 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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import numpy as np
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from hypothesis import assume, given, settings
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import hypothesis.strategies as st
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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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import unittest
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class TestGroupConvolution(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(1, 5),
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size=st.integers(7, 10),
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group=st.integers(1, 4),
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input_channels_per_group=st.integers(1, 8),
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output_channels_per_group=st.integers(1, 8),
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batch_size=st.integers(1, 3),
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# TODO(jiayq): if needed, add NHWC support.
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order=st.sampled_from(["NCHW"]),
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# Note: Eigen does not support group convolution, but it should
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# fall back to the default engine without failing.
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engine=st.sampled_from(["", "CUDNN", "EIGEN"]),
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use_bias=st.booleans(),
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**hu.gcs)
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@settings(max_examples=2, timeout=100)
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def test_group_convolution(
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self, stride, pad, kernel, size, group,
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input_channels_per_group, output_channels_per_group, batch_size,
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order, engine, use_bias, gc, dc):
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assume(size >= kernel)
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input_channels = input_channels_per_group * group
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output_channels = output_channels_per_group * group
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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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kernel=kernel,
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pad=pad,
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order=order,
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engine=engine,
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group=group,
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)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels, kernel, kernel,
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input_channels_per_group).astype(np.float32)\
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- 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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if order == "NCHW":
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X = X.transpose((0, 3, 1, 2))
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w = w.transpose((0, 3, 1, 2))
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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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for i in range(len(inputs)):
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self.assertGradientChecks(gc, op, inputs, i, [0])
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if __name__ == "__main__":
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unittest.main()
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