mirror of
https://github.com/saymrwulf/pytorch.git
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ccfaf46431
Summary: This is mostly for reusing all the cudnn test cases in our python operator_tests. Pull Request resolved: https://github.com/pytorch/pytorch/pull/12278 Differential Revision: D10842592 Pulled By: bddppq fbshipit-source-id: 4b3ed91fca64ff02060837b3270393bc2f9a9898
79 lines
2.7 KiB
Python
79 lines
2.7 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.proto import caffe2_pb2
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from caffe2.python import core
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import caffe2.python.hip_test_util as hiputl
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import caffe2.python.hypothesis_test_util as hu
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import unittest
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import os
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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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order=st.sampled_from(["NCHW", "NHWC"]),
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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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if hiputl.run_in_hip(gc, dc):
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if order == "NHWC":
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assume(group == 1 and engine != "CUDNN")
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else:
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# TODO: Group conv in NHWC not implemented for GPU yet.
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assume(group == 1 or order == "NCHW" or gc.device_type != caffe2_pb2.CUDA)
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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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