6762ca15c4
The session returned by cached_session uses soft placement, something we don't want for XLA_* devices. With soft placement ops lacking XLA kernels silently fall back and run on the CPU, misleading us into thinking we have more test coverage than we actually do. With this test some tests (rightly) start failing because they were testing ops with dtypes the XLA kernels do not support. I've removed these dtypes from the tests. This CL partially addresses b/132430685. It stubs out "cached_session" and "test_session" to raise errors, so we have more confidence that the compiler is being exercised. However, we still use XLA_* devices to exercise XLA, which has a different code path than xla.compile and tpu.rewrite. This needs to be incrementally fixed. PiperOrigin-RevId: 248437673
234 lines
9.6 KiB
Python
234 lines
9.6 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 __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 six.moves import xrange # pylint: disable=redefined-builtin
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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 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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# Test cloned from
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# tensorflow/python/kernel_tests/conv3d_backprop_filter_v2_grad_test.py
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class Conv3DBackpropFilterV2GradTest(xla_test.XLATestCase):
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def testGradient(self):
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with self.session(), self.test_scope():
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for padding in ["SAME", "VALID"]:
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for stride in [1, 2]:
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np.random.seed(1)
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in_shape = [2, 4, 3, 3, 2]
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in_val = constant_op.constant(
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2 * np.random.random_sample(in_shape) - 1, dtype=dtypes.float32)
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filter_shape = [3, 3, 3, 2, 3]
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strides = [1, stride, stride, stride, 1]
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# Make a convolution op with the current settings, just to easily get
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# the shape of the output.
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conv_out = nn_ops.conv3d(in_val,
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array_ops.zeros(filter_shape), strides,
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padding)
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out_backprop_shape = conv_out.get_shape().as_list()
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out_backprop_val = constant_op.constant(
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2 * np.random.random_sample(out_backprop_shape) - 1,
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dtype=dtypes.float32)
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output = nn_ops.conv3d_backprop_filter_v2(in_val, filter_shape,
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out_backprop_val, strides,
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padding)
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err = gradient_checker.compute_gradient_error(
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[in_val, out_backprop_val], [in_shape, out_backprop_shape],
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output, filter_shape)
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print("conv3d_backprop_filter gradient err = %g " % err)
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err_tolerance = 1e-3
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self.assertLess(err, err_tolerance)
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# Test cloned from tensorflow/python/kernel_tests/conv3d_transpose_test.py
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class Conv3DTransposeTest(xla_test.XLATestCase):
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def testConv3DTransposeSingleStride(self):
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with self.session(), self.test_scope():
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strides = [1, 1, 1, 1, 1]
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# Input, output: [batch, depth, height, width, channel]
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x_shape = [2, 5, 6, 4, 3]
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y_shape = [2, 5, 6, 4, 2]
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# Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
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f_shape = [3, 3, 3, 2, 3]
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x = constant_op.constant(
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1.0, shape=x_shape, name="x", dtype=dtypes.float32)
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f = constant_op.constant(
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1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
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output = nn_ops.conv3d_transpose(
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x, f, y_shape, strides=strides, padding="SAME")
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value = self.evaluate(output)
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# We count the number of cells being added at the locations in the output.
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# At the center, #cells = kernel_depth * kernel_height * kernel_width
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# At the corners, #cells = ceil(kernel_depth/2) * ceil(kernel_height/2)
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# * ceil(kernel_width/2)
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# At the edges, #cells =
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# kernel_depth * ceil(kernel_height/2) * ceil(kernel_width/2) or
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# ceil(kernel_depth/2) * kernel_height * ceil(kernel_width/2) or
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# ceil(kernel_depth/2) * ceil(kernel_height/2) * kernel_width
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# At the borders, #cells =
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# ceil(kernel_depth/2) * kernel_height * kernel_width or
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# kernel_depth * ceil(kernel_height/2) * kernel_width or
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# kernel_depth * kernel_height * ceil(kernel_width/2)
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for n in xrange(x_shape[0]):
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for k in xrange(f_shape[3]):
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for w in xrange(y_shape[3]):
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for h in xrange(y_shape[2]):
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for d in xrange(y_shape[1]):
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d_in = d > 0 and d < y_shape[1] - 1
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h_in = h > 0 and h < y_shape[2] - 1
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w_in = w > 0 and w < y_shape[3] - 1
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if d_in + h_in + w_in == 3:
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target = 27 * 3.0
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elif d_in + h_in + w_in == 2:
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target = 18 * 3.0
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elif d_in or h_in or w_in:
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target = 12 * 3.0
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else:
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target = 8 * 3.0
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self.assertAllClose(target, value[n, d, h, w, k])
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def testConv3DTransposeSame(self):
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with self.session(), self.test_scope():
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strides = [1, 2, 2, 2, 1]
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# Input, output: [batch, depth, height, width, depth]
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x_shape = [2, 5, 6, 4, 3]
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y_shape = [2, 10, 12, 8, 2]
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# Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
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f_shape = [3, 3, 3, 2, 3]
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x = constant_op.constant(
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1.0, shape=x_shape, name="x", dtype=dtypes.float32)
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f = constant_op.constant(
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1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
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output = nn_ops.conv3d_transpose(
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x, f, y_shape, strides=strides, padding="SAME")
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value = self.evaluate(output)
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for n in xrange(x_shape[0]):
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for k in xrange(f_shape[3]):
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for w in xrange(y_shape[3]):
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for h in xrange(y_shape[2]):
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for d in xrange(y_shape[1]):
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# We add a case for locations divisible by the stride.
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d_in = d % strides[1] == 0 and 0 < d < y_shape[1] - 1
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h_in = h % strides[2] == 0 and 0 < h < y_shape[2] - 1
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w_in = w % strides[3] == 0 and 0 < w < y_shape[3] - 1
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if d_in + h_in + w_in == 3:
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target = 8 * 3.0
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elif d_in + h_in + w_in == 2:
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target = 4 * 3.0
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elif d_in or h_in or w_in:
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target = 2 * 3.0
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else:
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target = 3.0
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self.assertAllClose(target, value[n, d, h, w, k])
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def testConv3DTransposeValid(self):
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with self.session(), self.test_scope():
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strides = [1, 2, 2, 2, 1]
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# Input, output: [batch, depth, height, width, depth]
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x_shape = [2, 5, 6, 4, 3]
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y_shape = [2, 11, 13, 9, 2]
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# Filter: [kernel_depth, kernel_height, kernel_width, out_depth, in_depth]
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f_shape = [3, 3, 3, 2, 3]
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x = constant_op.constant(
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1.0, shape=x_shape, name="x", dtype=dtypes.float32)
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f = constant_op.constant(
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1.0, shape=f_shape, name="filter", dtype=dtypes.float32)
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output = nn_ops.conv3d_transpose(
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x, f, y_shape, strides=strides, padding="VALID")
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value = self.evaluate(output)
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cache_values = np.zeros(y_shape, dtype=np.float32)
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# The amount of padding added
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pad = 1
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for n in xrange(x_shape[0]):
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for k in xrange(f_shape[3]):
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for w in xrange(y_shape[3]):
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for h in xrange(y_shape[2]):
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for d in xrange(y_shape[1]):
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# We add a case for locations divisible by the stride.
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d_in = d % strides[1] == 0 and pad < d < y_shape[1] - 1 - pad
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h_in = h % strides[2] == 0 and pad < h < y_shape[2] - 1 - pad
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w_in = w % strides[3] == 0 and pad < w < y_shape[3] - 1 - pad
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if d_in + h_in + w_in == 3:
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target = 8 * 3.0
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elif d_in + h_in + w_in == 2:
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target = 4 * 3.0
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elif d_in or h_in or w_in:
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target = 2 * 3.0
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else:
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target = 3.0
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cache_values[n, d, h, w, k] = target
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# copy values in the border
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cache_values[n, :, :, 0, k] = cache_values[n, :, :, 1, k]
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cache_values[n, :, :, -1, k] = cache_values[n, :, :, -2, k]
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cache_values[n, :, 0, :, k] = cache_values[n, :, 1, :, k]
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cache_values[n, :, -1, :, k] = cache_values[n, :, -2, :, k]
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cache_values[n, 0, :, :, k] = cache_values[n, 1, :, :, k]
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cache_values[n, -1, :, :, k] = cache_values[n, -2, :, :, k]
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self.assertAllClose(cache_values, value)
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def testGradient(self):
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x_shape = [2, 3, 4, 3, 2]
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f_shape = [3, 3, 3, 2, 2]
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y_shape = [2, 6, 8, 6, 2]
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strides = [1, 2, 2, 2, 1]
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np.random.seed(1) # Make it reproducible.
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x_val = np.random.random_sample(x_shape).astype(np.float64)
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f_val = np.random.random_sample(f_shape).astype(np.float64)
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with self.session(), self.test_scope():
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x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
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f = constant_op.constant(f_val, name="f", dtype=dtypes.float32)
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output = nn_ops.conv3d_transpose(
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x, f, y_shape, strides=strides, padding="SAME")
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err = gradient_checker.compute_gradient_error([x, f], [x_shape, f_shape],
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output, y_shape)
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print("conv3d_transpose gradient err = %g " % err)
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err_tolerance = 0.001
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self.assertLess(err, err_tolerance)
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if __name__ == "__main__":
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googletest.main()
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