wehub-resource-sync
861 lines
28 KiB
Python
861 lines
28 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for 3D convolutions using the XLA JIT."""
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.compiler.tests import test_utils
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_nn_ops
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from tensorflow.python.ops import gradient_checker
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from tensorflow.python.ops import nn_ops
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import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
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from tensorflow.python.platform import googletest
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CONV_CONFIGS = (
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("_Conv3D_data_format_NDHWC", "NDHWC", "Conv3D"),
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("_Conv3D_data_format_NCDHW", "NCDHW", "Conv3D"),
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("_Conv_data_format_NDHWC", "NDHWC", "Conv"),
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("_Conv_data_format_NCDHW", "NCDHW", "Conv"),
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)
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# Test outputs computed in prod (colab) by running nn.conv3d on a GPU device
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# with its GPU (non-xla) kernel.
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class Conv3DTest(xla_test.XLATestCase, parameterized.TestCase):
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def _VerifyValues(
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self,
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input_sizes=None,
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filter_sizes=None,
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strides=None,
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dilations=None,
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padding=None,
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data_format_src="NDHWC",
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data_format_dst="NDHWC",
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expected=None,
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op_name="Conv3D",
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):
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"""Tests that tf.nn.conv3d produces the expected value.
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Args:
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input_sizes: Input tensor dimensions in [batch, input_rows, input_cols,
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input_depth].
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filter_sizes: Filter tensor dimensions in [kernel_rows, kernel_cols,
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input_depth, output_depth].
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strides: Strides.
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dilations: RHS dilations.
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padding: Padding type.
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data_format_src: Data format input is in.
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data_format_dst: Data format verification will run and input is converted
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to.
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expected: Expected output.
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op_name: Name of operation to test (Conv/Conv2D)
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"""
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total_size_1 = np.prod(input_sizes)
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total_size_2 = np.prod(filter_sizes)
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x1 = np.reshape(
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[f * 1.0 / total_size_1 for f in range(1, total_size_1 + 1)],
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input_sizes,
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)
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x2 = np.reshape(
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[f * 1.0 / total_size_2 for f in range(1, total_size_2 + 1)],
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filter_sizes,
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)
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strides = [1] + strides + [1]
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if dilations is None:
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dilations = [1, 1, 1]
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dilations = [1] + dilations + [1]
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# Convert between data formats.
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expected = test_utils.ConvertBetweenDataFormats(
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expected, data_format_src, data_format_dst
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)
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x1 = test_utils.ConvertBetweenDataFormats(
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x1, data_format_src, data_format_dst
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)
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input_sizes = test_utils.PermuteDimsBetweenDataFormats(
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input_sizes, data_format_src, data_format_dst
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)
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strides = test_utils.PermuteDimsBetweenDataFormats(
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strides, data_format_src, data_format_dst
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)
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dilations = test_utils.PermuteDimsBetweenDataFormats(
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dilations, data_format_src, data_format_dst
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)
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with self.session() as sess:
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t1 = array_ops.placeholder(dtypes.bfloat16, shape=input_sizes)
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t2 = array_ops.placeholder(dtypes.bfloat16, shape=filter_sizes)
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with self.test_scope():
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if op_name == "Conv":
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conv_format = (
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"CHANNELS_LAST"
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if data_format_dst == "NDHWC"
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else "CHANNELS_FIRST"
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)
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out = gen_nn_ops.conv(
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t1,
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t2,
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strides=strides,
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padding=padding,
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data_format=conv_format,
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dilations=dilations,
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)
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elif op_name == "Conv3D":
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out = nn_ops.conv3d(
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t1,
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t2,
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strides=strides,
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padding=padding,
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data_format=data_format_dst,
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dilations=dilations,
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)
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else:
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raise ValueError("Invalid op name: %s" % op_name)
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value = sess.run(out, {t1: x1, t2: x2})
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self.assertAllCloseAccordingToType(expected, value)
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@parameterized.named_parameters(*CONV_CONFIGS)
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def testConv3D1x1x1Filter(self, data_format, op_name):
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expected_output = np.reshape(
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[
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0.18518518518518517,
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0.2222222222222222,
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0.25925925925925924,
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0.4074074074074074,
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0.5,
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0.5925925925925926,
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0.6296296296296297,
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0.7777777777777777,
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0.9259259259259259,
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0.8518518518518519,
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1.0555555555555556,
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1.259259259259259,
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1.074074074074074,
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1.3333333333333333,
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1.5925925925925926,
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1.2962962962962963,
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1.6111111111111112,
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1.9259259259259258,
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],
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[1, 2, 3, 1, 3],
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)
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# These are equivalent to the Conv2D1x1 case.
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self._VerifyValues(
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input_sizes=[1, 2, 3, 1, 3],
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filter_sizes=[1, 1, 1, 3, 3],
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strides=[1, 1, 1],
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padding="VALID",
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expected=expected_output,
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data_format_src="NDHWC",
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data_format_dst=data_format,
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op_name=op_name,
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)
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self._VerifyValues(
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input_sizes=[1, 2, 1, 3, 3],
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filter_sizes=[1, 1, 1, 3, 3],
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strides=[1, 1, 1],
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padding="VALID",
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expected=np.reshape(expected_output, [1, 2, 1, 3, 3]),
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data_format_src="NDHWC",
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data_format_dst=data_format,
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op_name=op_name,
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)
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self._VerifyValues(
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input_sizes=[1, 1, 2, 3, 3],
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filter_sizes=[1, 1, 1, 3, 3],
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strides=[1, 1, 1],
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padding="VALID",
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expected=np.reshape(expected_output, [1, 1, 2, 3, 3]),
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data_format_src="NDHWC",
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data_format_dst=data_format,
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op_name=op_name,
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)
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@parameterized.named_parameters(*CONV_CONFIGS)
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def testConv3D1x1x1Filter2x1x1Dilation(self, data_format, op_name):
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expected_output = np.reshape(
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[
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0.05555555555555555,
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0.1111111111111111,
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0.16666666666666666,
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0.2222222222222222,
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0.2777777777777778,
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0.3333333333333333,
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0.3888888888888889,
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0.4444444444444444,
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0.5,
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0.5555555555555556,
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0.6111111111111112,
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0.6666666666666666,
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0.7222222222222222,
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0.7777777777777778,
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0.8333333333333334,
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0.8888888888888888,
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0.9444444444444444,
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1.0,
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],
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[1, 3, 6, 1, 1],
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)
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self._VerifyValues(
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input_sizes=[1, 3, 6, 1, 1],
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filter_sizes=[1, 1, 1, 1, 1],
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strides=[1, 1, 1],
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padding="VALID",
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dilations=[2, 1, 1],
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expected=expected_output,
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data_format_src="NDHWC",
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data_format_dst=data_format,
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op_name=op_name,
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)
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# Expected values computed using scipy's correlate function.
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@parameterized.named_parameters(*CONV_CONFIGS)
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def testConv3D2x2x2Filter(self, data_format, op_name):
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expected_output = np.reshape(
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[
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3.7719907407407405,
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3.850694444444445,
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3.929398148148149,
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4.265046296296295,
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4.357638888888888,
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4.450231481481481,
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6.730324074074074,
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6.892361111111109,
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7.054398148148148,
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7.223379629629629,
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7.399305555555557,
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7.575231481481481,
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9.688657407407408,
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9.934027777777779,
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10.17939814814815,
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10.181712962962962,
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10.440972222222221,
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10.700231481481481,
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],
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[1, 3, 1, 2, 3],
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)
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# expected_shape = [1, 3, 1, 2, 5]
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self._VerifyValues(
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input_sizes=[1, 4, 2, 3, 3], # b, z, y, x, fin
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filter_sizes=[2, 2, 2, 3, 3], # z, y, x, fin, fout
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strides=[1, 1, 1],
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padding="VALID",
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expected=expected_output,
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data_format_src="NDHWC",
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data_format_dst=data_format,
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op_name=op_name,
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)
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@parameterized.named_parameters(*CONV_CONFIGS)
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def testConv3D2x2x2Filter1x2x1Dilation(self, data_format, op_name):
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expected_output = np.reshape(
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[
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1.1388888888888888,
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1.2013888888888888,
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1.3263888888888888,
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1.3888888888888888,
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1.5138888888888888,
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1.5763888888888888,
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1.701388888888889,
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1.763888888888889,
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2.263888888888889,
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2.3263888888888893,
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2.451388888888889,
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2.513888888888889,
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2.6388888888888893,
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2.701388888888889,
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2.826388888888889,
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2.888888888888889,
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3.388888888888889,
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3.451388888888889,
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3.576388888888889,
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3.6388888888888884,
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3.7638888888888893,
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3.8263888888888893,
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3.9513888888888893,
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4.013888888888889,
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],
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[1, 3, 4, 2, 1],
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)
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self._VerifyValues(
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input_sizes=[1, 4, 6, 3, 1],
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filter_sizes=[2, 2, 2, 1, 1],
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strides=[1, 1, 1],
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padding="VALID",
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dilations=[1, 2, 1],
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expected=expected_output,
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data_format_src="NDHWC",
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data_format_dst=data_format,
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op_name=op_name,
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)
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@parameterized.named_parameters(*CONV_CONFIGS)
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def testConv3DStrides(self, data_format, op_name):
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expected_output = np.reshape(
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[
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0.06071428571428571,
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0.08988095238095238,
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0.10238095238095238,
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0.11488095238095238,
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0.12738095238095237,
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0.13988095238095238,
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0.08452380952380953,
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0.26071428571428573,
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0.35238095238095235,
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0.36488095238095236,
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0.3773809523809524,
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0.3898809523809524,
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0.4023809523809524,
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0.23452380952380952,
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0.46071428571428574,
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0.6148809523809524,
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0.6273809523809524,
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0.6398809523809523,
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0.6523809523809524,
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0.6648809523809525,
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0.3845238095238095,
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1.1273809523809524,
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1.4898809523809524,
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1.5023809523809524,
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1.5148809523809523,
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1.5273809523809523,
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1.5398809523809525,
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0.8845238095238095,
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1.3273809523809526,
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1.7523809523809522,
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1.764880952380952,
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1.7773809523809523,
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1.7898809523809525,
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1.8023809523809526,
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1.0345238095238096,
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1.5273809523809525,
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2.0148809523809526,
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2.0273809523809523,
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2.0398809523809525,
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2.052380952380952,
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2.0648809523809524,
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1.1845238095238095,
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2.1940476190476192,
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2.8898809523809526,
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2.9023809523809527,
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2.9148809523809525,
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2.9273809523809526,
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2.9398809523809524,
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1.6845238095238095,
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2.394047619047619,
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3.1523809523809523,
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3.1648809523809525,
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3.177380952380952,
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3.1898809523809524,
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3.2023809523809526,
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1.8345238095238097,
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2.594047619047619,
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3.4148809523809525,
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3.427380952380952,
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3.4398809523809524,
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3.4523809523809526,
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3.4648809523809523,
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1.9845238095238096,
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],
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[1, 3, 3, 7, 1],
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)
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self._VerifyValues(
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input_sizes=[1, 5, 8, 7, 1],
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filter_sizes=[1, 2, 3, 1, 1],
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strides=[2, 3, 1], # different stride for each spatial dimension
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padding="SAME",
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expected=expected_output,
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data_format_src="NDHWC",
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data_format_dst=data_format,
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op_name=op_name,
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)
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@parameterized.named_parameters(*CONV_CONFIGS)
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def testConv3D2x2x2FilterStride2(self, data_format, op_name):
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expected_output = np.reshape(
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[
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3.7719907407407405,
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3.850694444444445,
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3.929398148148149,
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9.688657407407408,
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9.934027777777779,
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10.17939814814815,
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],
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[1, 2, 1, 1, 3],
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)
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self._VerifyValues(
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input_sizes=[1, 4, 2, 3, 3],
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filter_sizes=[2, 2, 2, 3, 3],
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strides=[2, 2, 2],
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padding="VALID",
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expected=expected_output,
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data_format_src="NDHWC",
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data_format_dst=data_format,
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op_name=op_name,
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)
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@parameterized.named_parameters(*CONV_CONFIGS)
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def testConv3DStride3(self, data_format, op_name):
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expected_output = np.reshape(
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[
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1.5114087301587302,
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1.5716765873015872,
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1.6319444444444446,
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1.5634920634920635,
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1.6267361111111112,
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1.6899801587301588,
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1.6155753968253967,
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1.681795634920635,
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1.748015873015873,
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1.9280753968253967,
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2.012152777777778,
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2.096230158730159,
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1.9801587301587302,
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2.067212301587302,
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2.154265873015873,
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2.0322420634920637,
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2.122271825396825,
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2.2123015873015874,
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4.428075396825396,
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4.65500992063492,
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4.881944444444444,
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4.480158730158729,
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4.710069444444444,
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4.939980158730158,
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4.532242063492063,
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4.7651289682539675,
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4.9980158730158735,
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4.844742063492064,
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5.095486111111112,
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5.346230158730158,
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4.896825396825397,
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5.150545634920635,
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5.4042658730158735,
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4.94890873015873,
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5.205605158730158,
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5.462301587301588,
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],
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[1, 2, 2, 3, 3],
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)
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self._VerifyValues(
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input_sizes=[1, 6, 7, 8, 2],
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filter_sizes=[3, 2, 1, 2, 3],
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strides=[3, 3, 3],
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padding="VALID",
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expected=expected_output,
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data_format_src="NDHWC",
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data_format_dst=data_format,
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op_name=op_name,
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)
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@parameterized.named_parameters(*CONV_CONFIGS)
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def testConv3D2x2x2FilterStride2Same(self, data_format, op_name):
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expected_output = np.reshape(
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[
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3.7719907407407405,
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3.850694444444445,
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3.929398148148149,
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2.0162037037037037,
|
|
2.0659722222222223,
|
|
2.1157407407407405,
|
|
9.688657407407408,
|
|
9.934027777777779,
|
|
10.17939814814815,
|
|
4.599537037037037,
|
|
4.732638888888889,
|
|
4.8657407407407405,
|
|
],
|
|
[1, 2, 1, 2, 3],
|
|
)
|
|
self._VerifyValues(
|
|
input_sizes=[1, 4, 2, 3, 3],
|
|
filter_sizes=[2, 2, 2, 3, 3],
|
|
strides=[2, 2, 2],
|
|
padding="SAME",
|
|
expected=expected_output,
|
|
data_format_src="NDHWC",
|
|
data_format_dst=data_format,
|
|
op_name=op_name,
|
|
)
|
|
|
|
@parameterized.named_parameters(*CONV_CONFIGS)
|
|
def testKernelSmallerThanStride(self, data_format, op_name):
|
|
expected_output = np.reshape(
|
|
[
|
|
0.037037037037037035,
|
|
0.1111111111111111,
|
|
0.25925925925925924,
|
|
0.3333333333333333,
|
|
0.7037037037037037,
|
|
0.7777777777777778,
|
|
0.9259259259259259,
|
|
1.0,
|
|
],
|
|
[1, 2, 2, 2, 1],
|
|
)
|
|
|
|
self._VerifyValues(
|
|
input_sizes=[1, 3, 3, 3, 1],
|
|
filter_sizes=[1, 1, 1, 1, 1],
|
|
strides=[2, 2, 2],
|
|
padding="SAME",
|
|
expected=expected_output,
|
|
data_format_src="NDHWC",
|
|
data_format_dst=data_format,
|
|
op_name=op_name,
|
|
)
|
|
self._VerifyValues(
|
|
input_sizes=[1, 3, 3, 3, 1],
|
|
filter_sizes=[1, 1, 1, 1, 1],
|
|
strides=[2, 2, 2],
|
|
padding="VALID",
|
|
expected=expected_output,
|
|
data_format_src="NDHWC",
|
|
data_format_dst=data_format,
|
|
op_name=op_name,
|
|
)
|
|
|
|
expected_output = np.reshape(
|
|
[
|
|
0.5408163265306123,
|
|
0.5801749271137027,
|
|
0.28061224489795916,
|
|
0.8163265306122448,
|
|
0.8556851311953353,
|
|
0.4030612244897959,
|
|
0.41873177842565595,
|
|
0.43403790087463556,
|
|
0.19642857142857142,
|
|
2.4693877551020407,
|
|
2.5087463556851315,
|
|
1.1377551020408163,
|
|
2.7448979591836733,
|
|
2.7842565597667637,
|
|
1.260204081632653,
|
|
1.168731778425656,
|
|
1.1840379008746356,
|
|
0.5178571428571429,
|
|
1.0951166180758019,
|
|
1.1060495626822158,
|
|
0.4464285714285714,
|
|
1.1716472303206997,
|
|
1.1825801749271136,
|
|
0.4770408163265306,
|
|
0.3691690962099125,
|
|
0.37244897959183676,
|
|
0.125,
|
|
],
|
|
[1, 3, 3, 3, 1],
|
|
)
|
|
self._VerifyValues(
|
|
input_sizes=[1, 7, 7, 7, 1],
|
|
filter_sizes=[2, 2, 2, 1, 1],
|
|
strides=[3, 3, 3],
|
|
padding="SAME",
|
|
expected=expected_output,
|
|
data_format_src="NDHWC",
|
|
data_format_dst=data_format,
|
|
op_name=op_name,
|
|
)
|
|
|
|
expected_output = np.reshape(
|
|
[
|
|
0.5408163265306123,
|
|
0.5801749271137027,
|
|
0.8163265306122448,
|
|
0.8556851311953353,
|
|
2.4693877551020407,
|
|
2.5087463556851315,
|
|
2.7448979591836733,
|
|
2.7842565597667637,
|
|
],
|
|
[1, 2, 2, 2, 1],
|
|
)
|
|
self._VerifyValues(
|
|
input_sizes=[1, 7, 7, 7, 1],
|
|
filter_sizes=[2, 2, 2, 1, 1],
|
|
strides=[3, 3, 3],
|
|
padding="VALID",
|
|
expected=expected_output,
|
|
data_format_src="NDHWC",
|
|
data_format_dst=data_format,
|
|
op_name=op_name,
|
|
)
|
|
|
|
@parameterized.named_parameters(*CONV_CONFIGS)
|
|
def testKernelSizeMatchesInputSize(self, data_format, op_name):
|
|
expected_output = np.reshape([1.5625, 1.875], [1, 1, 1, 1, 2])
|
|
self._VerifyValues(
|
|
input_sizes=[1, 2, 1, 2, 1],
|
|
filter_sizes=[2, 1, 2, 1, 2],
|
|
strides=[1, 1, 1],
|
|
padding="VALID",
|
|
expected=expected_output,
|
|
data_format_src="NDHWC",
|
|
data_format_dst=data_format,
|
|
op_name=op_name,
|
|
)
|
|
|
|
def testConvExpandedBatch(self):
|
|
tensor_in_sizes_batch = [10, 2, 3, 1, 3]
|
|
tensor_in_sizes_expanded_batch = [2, 5, 2, 3, 1, 3]
|
|
batch_dims = 2
|
|
filter_in_sizes = [1, 1, 1, 3, 3]
|
|
filter_in = np.arange(
|
|
1, np.prod(filter_in_sizes) + 1, dtype=np.float32
|
|
).reshape(filter_in_sizes)
|
|
x1 = np.arange(
|
|
1, np.prod(tensor_in_sizes_batch) + 1, dtype=np.float32
|
|
).reshape(tensor_in_sizes_batch)
|
|
x2 = x1.reshape(tensor_in_sizes_expanded_batch)
|
|
|
|
with self.session() as sess:
|
|
t1 = array_ops.placeholder(dtypes.bfloat16, shape=tensor_in_sizes_batch)
|
|
t2 = array_ops.placeholder(
|
|
dtypes.bfloat16, shape=tensor_in_sizes_expanded_batch
|
|
)
|
|
filter_t = array_ops.placeholder(dtypes.bfloat16, shape=filter_in_sizes)
|
|
|
|
out1 = gen_nn_ops.conv(
|
|
t1, filter_t, strides=[1, 1, 1, 1, 1], padding="VALID"
|
|
)
|
|
out2 = gen_nn_ops.conv(
|
|
t2,
|
|
filter_t,
|
|
strides=[1, 1, 1, 1, 1],
|
|
padding="VALID",
|
|
batch_dims=batch_dims,
|
|
)
|
|
value1 = sess.run(out1, {t1: x1, filter_t: filter_in})
|
|
value2 = sess.run(out2, {t2: x2, filter_t: filter_in})
|
|
|
|
self.assertEqual(list(value1.shape), tensor_in_sizes_batch)
|
|
self.assertEqual(list(value2.shape), tensor_in_sizes_expanded_batch)
|
|
self.assertAllCloseAccordingToType(value1, value2.reshape(value1.shape))
|
|
|
|
|
|
# Test cloned from
|
|
# tensorflow/python/kernel_tests/conv3d_backprop_filter_v2_grad_test.py
|
|
class Conv3DBackpropFilterV2GradTest(xla_test.XLATestCase):
|
|
|
|
def testGradient(self):
|
|
with self.session(), self.test_scope():
|
|
for padding in ["SAME", "VALID"]:
|
|
for stride in [1, 2]:
|
|
np.random.seed(1)
|
|
in_shape = [2, 4, 3, 3, 2]
|
|
in_val = constant_op.constant(
|
|
2 * np.random.random_sample(in_shape) - 1, dtype=dtypes.float32)
|
|
filter_shape = [3, 3, 3, 2, 3]
|
|
strides = [1, stride, stride, stride, 1]
|
|
# Make a convolution op with the current settings, just to easily get
|
|
# the shape of the output.
|
|
conv_out = nn_ops.conv3d(in_val,
|
|
array_ops.zeros(filter_shape), strides,
|
|
padding)
|
|
out_backprop_shape = conv_out.get_shape().as_list()
|
|
out_backprop_val = constant_op.constant(
|
|
2 * np.random.random_sample(out_backprop_shape) - 1,
|
|
dtype=dtypes.float32)
|
|
output = nn_ops.conv3d_backprop_filter_v2(in_val, filter_shape,
|
|
out_backprop_val, strides,
|
|
padding)
|
|
err = gradient_checker.compute_gradient_error(
|
|
[in_val, out_backprop_val], [in_shape, out_backprop_shape],
|
|
output, filter_shape)
|
|
print("conv3d_backprop_filter gradient err = %g " % err)
|
|
err_tolerance = 1e-3
|
|
self.assertLess(err, err_tolerance)
|
|
|
|
|
|
# Test cloned from tensorflow/python/kernel_tests/conv3d_transpose_test.py
|
|
class Conv3DTransposeTest(xla_test.XLATestCase):
|
|
|
|
def testConv3DTransposeSingleStride(self):
|
|
with self.session(), self.test_scope():
|
|
strides = [1, 1, 1, 1, 1]
|
|
|
|
# Input, output: [batch, depth, height, width, channel]
|
|
x_shape = [2, 5, 6, 4, 3]
|
|
y_shape = [2, 5, 6, 4, 2]
|
|
|
|
# Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
|
|
f_shape = [3, 3, 3, 2, 3]
|
|
|
|
x = constant_op.constant(
|
|
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
|
|
f = constant_op.constant(
|
|
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
|
|
output = nn_ops.conv3d_transpose(
|
|
x, f, y_shape, strides=strides, padding="SAME")
|
|
value = self.evaluate(output)
|
|
|
|
# We count the number of cells being added at the locations in the output.
|
|
# At the center, #cells = kernel_depth * kernel_height * kernel_width
|
|
# At the corners, #cells = ceil(kernel_depth/2) * ceil(kernel_height/2)
|
|
# * ceil(kernel_width/2)
|
|
# At the edges, #cells =
|
|
# kernel_depth * ceil(kernel_height/2) * ceil(kernel_width/2) or
|
|
# ceil(kernel_depth/2) * kernel_height * ceil(kernel_width/2) or
|
|
# ceil(kernel_depth/2) * ceil(kernel_height/2) * kernel_width
|
|
# At the borders, #cells =
|
|
# ceil(kernel_depth/2) * kernel_height * kernel_width or
|
|
# kernel_depth * ceil(kernel_height/2) * kernel_width or
|
|
# kernel_depth * kernel_height * ceil(kernel_width/2)
|
|
|
|
for n in range(x_shape[0]):
|
|
for k in range(f_shape[3]):
|
|
for w in range(y_shape[3]):
|
|
for h in range(y_shape[2]):
|
|
for d in range(y_shape[1]):
|
|
d_in = d > 0 and d < y_shape[1] - 1
|
|
h_in = h > 0 and h < y_shape[2] - 1
|
|
w_in = w > 0 and w < y_shape[3] - 1
|
|
if d_in + h_in + w_in == 3:
|
|
target = 27 * 3.0
|
|
elif d_in + h_in + w_in == 2:
|
|
target = 18 * 3.0
|
|
elif d_in or h_in or w_in:
|
|
target = 12 * 3.0
|
|
else:
|
|
target = 8 * 3.0
|
|
self.assertAllClose(target, value[n, d, h, w, k])
|
|
|
|
def testConv3DTransposeSame(self):
|
|
with self.session(), self.test_scope():
|
|
strides = [1, 2, 2, 2, 1]
|
|
|
|
# Input, output: [batch, depth, height, width, depth]
|
|
x_shape = [2, 5, 6, 4, 3]
|
|
y_shape = [2, 10, 12, 8, 2]
|
|
|
|
# Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
|
|
f_shape = [3, 3, 3, 2, 3]
|
|
|
|
x = constant_op.constant(
|
|
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
|
|
f = constant_op.constant(
|
|
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
|
|
output = nn_ops.conv3d_transpose(
|
|
x, f, y_shape, strides=strides, padding="SAME")
|
|
value = self.evaluate(output)
|
|
|
|
for n in range(x_shape[0]):
|
|
for k in range(f_shape[3]):
|
|
for w in range(y_shape[3]):
|
|
for h in range(y_shape[2]):
|
|
for d in range(y_shape[1]):
|
|
# We add a case for locations divisible by the stride.
|
|
d_in = d % strides[1] == 0 and 0 < d < y_shape[1] - 1
|
|
h_in = h % strides[2] == 0 and 0 < h < y_shape[2] - 1
|
|
w_in = w % strides[3] == 0 and 0 < w < y_shape[3] - 1
|
|
if d_in + h_in + w_in == 3:
|
|
target = 8 * 3.0
|
|
elif d_in + h_in + w_in == 2:
|
|
target = 4 * 3.0
|
|
elif d_in or h_in or w_in:
|
|
target = 2 * 3.0
|
|
else:
|
|
target = 3.0
|
|
self.assertAllClose(target, value[n, d, h, w, k])
|
|
|
|
def testConv3DTransposeValid(self):
|
|
with self.session(), self.test_scope():
|
|
strides = [1, 2, 2, 2, 1]
|
|
|
|
# Input, output: [batch, depth, height, width, depth]
|
|
x_shape = [2, 5, 6, 4, 3]
|
|
y_shape = [2, 11, 13, 9, 2]
|
|
|
|
# Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
|
|
f_shape = [3, 3, 3, 2, 3]
|
|
|
|
x = constant_op.constant(
|
|
1.0, shape=x_shape, name="x", dtype=dtypes.float32)
|
|
f = constant_op.constant(
|
|
1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
|
|
output = nn_ops.conv3d_transpose(
|
|
x, f, y_shape, strides=strides, padding="VALID")
|
|
value = self.evaluate(output)
|
|
|
|
cache_values = np.zeros(y_shape, dtype=np.float32)
|
|
|
|
# The amount of padding added
|
|
pad = 1
|
|
|
|
for n in range(x_shape[0]):
|
|
for k in range(f_shape[3]):
|
|
for w in range(y_shape[3]):
|
|
for h in range(y_shape[2]):
|
|
for d in range(y_shape[1]):
|
|
# We add a case for locations divisible by the stride.
|
|
d_in = d % strides[1] == 0 and pad < d < y_shape[1] - 1 - pad
|
|
h_in = h % strides[2] == 0 and pad < h < y_shape[2] - 1 - pad
|
|
w_in = w % strides[3] == 0 and pad < w < y_shape[3] - 1 - pad
|
|
if d_in + h_in + w_in == 3:
|
|
target = 8 * 3.0
|
|
elif d_in + h_in + w_in == 2:
|
|
target = 4 * 3.0
|
|
elif d_in or h_in or w_in:
|
|
target = 2 * 3.0
|
|
else:
|
|
target = 3.0
|
|
cache_values[n, d, h, w, k] = target
|
|
|
|
# copy values in the border
|
|
cache_values[n, :, :, 0, k] = cache_values[n, :, :, 1, k]
|
|
cache_values[n, :, :, -1, k] = cache_values[n, :, :, -2, k]
|
|
cache_values[n, :, 0, :, k] = cache_values[n, :, 1, :, k]
|
|
cache_values[n, :, -1, :, k] = cache_values[n, :, -2, :, k]
|
|
cache_values[n, 0, :, :, k] = cache_values[n, 1, :, :, k]
|
|
cache_values[n, -1, :, :, k] = cache_values[n, -2, :, :, k]
|
|
|
|
self.assertAllClose(cache_values, value)
|
|
|
|
def testGradient(self):
|
|
x_shape = [2, 3, 4, 3, 2]
|
|
f_shape = [3, 3, 3, 2, 2]
|
|
y_shape = [2, 6, 8, 6, 2]
|
|
strides = [1, 2, 2, 2, 1]
|
|
np.random.seed(1) # Make it reproducible.
|
|
x_val = np.random.random_sample(x_shape).astype(np.float64)
|
|
f_val = np.random.random_sample(f_shape).astype(np.float64)
|
|
with self.session(), self.test_scope():
|
|
x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
|
|
f = constant_op.constant(f_val, name="f", dtype=dtypes.float32)
|
|
output = nn_ops.conv3d_transpose(
|
|
x, f, y_shape, strides=strides, padding="SAME")
|
|
err = gradient_checker.compute_gradient_error([x, f], [x_shape, f_shape],
|
|
output, y_shape)
|
|
print("conv3d_transpose gradient err = %g " % err)
|
|
err_tolerance = 0.001
|
|
self.assertLess(err, err_tolerance)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
googletest.main()
|