mirror of
https://github.com/saymrwulf/pytorch.git
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5cd0f5e8ec
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/41645 Pull Request resolved: https://github.com/facebookresearch/pytext/pull/1405 Test Plan: buck test Reviewed By: thatch Differential Revision: D20323893 fbshipit-source-id: 54665d589568c4198e96a27f0ed8e5b41df7b86b
86 lines
2.9 KiB
Python
86 lines
2.9 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, utils
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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, deadline=None)
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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.CPU)
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if group != 1 and order == "NHWC":
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dc = [d for d in dc if d.device_type == caffe2_pb2.CPU]
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# Group conv not implemented with EIGEN engine.
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assume(group == 1 or engine != "EIGEN")
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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 = utils.NHWC2NCHW(X)
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w = utils.NHWC2NCHW(w)
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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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