549e69ca13
This extends the support just added for tf.nn.conv2d and keras.layers.Conv2D. PiperOrigin-RevId: 314738423 Change-Id: I588c36e493d1f41a67b9721e2cbd84c564277f44
127 lines
5.0 KiB
Python
127 lines
5.0 KiB
Python
# Copyright 2015 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 convolution related functionality in tensorflow.ops.nn."""
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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.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 nn_ops
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from tensorflow.python.platform import test
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class Conv1DTest(test.TestCase):
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def testBasic(self):
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"""Test that argument passing to conv1d is handled properly."""
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# double datatype is currently not supported for convolution ops
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# on the ROCm platform
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optional_float64 = [] if test.is_built_with_rocm() else [dtypes.float64]
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for dtype in [dtypes.float16, dtypes.float32] + optional_float64:
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x = constant_op.constant([1, 2, 3, 4], dtype=dtype)
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x = array_ops.expand_dims(x, 0) # Add batch dimension
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x = array_ops.expand_dims(x, 2) # And depth dimension
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filters = constant_op.constant([2, 1], dtype=dtype)
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filters = array_ops.expand_dims(filters, 1) # in_channels
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filters = array_ops.expand_dims(filters, 2) # out_channels
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# Filters is 2x1x1
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for stride in [1, 2]:
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with self.cached_session(use_gpu=test.is_gpu_available()):
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c = nn_ops.conv1d(x, filters, stride, padding="VALID")
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reduced = array_ops.squeeze(c)
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output = self.evaluate(reduced)
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if stride == 1:
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self.assertEqual(len(output), 3)
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self.assertAllClose(output,
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[2 * 1 + 1 * 2, 2 * 2 + 1 * 3, 2 * 3 + 1 * 4])
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else:
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self.assertEqual(len(output), 2)
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self.assertAllClose(output, [2 * 1 + 1 * 2, 2 * 3 + 1 * 4])
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def testExpandedBatch(self):
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"""Test that argument passing to conv1d is handled properly."""
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# double datatype is currently not supported for convolution ops
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# on the ROCm platform
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x = constant_op.constant([1, 2, 3, 4], dtype=dtypes.float32)
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x = array_ops.expand_dims(x, 0) # Add batch dimension
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x = array_ops.expand_dims(x, 2) # And depth dimension
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x = array_ops.stack([x, x]) # Make batch shape [2, 1]
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filters = constant_op.constant([2, 1], dtype=dtypes.float32)
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filters = array_ops.expand_dims(filters, 1) # in_channels
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filters = array_ops.expand_dims(filters, 2) # out_channels
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# Filters is 2x1x1
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for stride in [1, 2]:
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with self.cached_session(use_gpu=test.is_gpu_available()):
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c = nn_ops.conv1d(x, filters, stride, padding="VALID")
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reduced = array_ops.squeeze(c) # Sequeeze out dims 1 and 3.
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output = self.evaluate(reduced)
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if stride == 1:
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self.assertAllClose(output,
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[[2 * 1 + 1 * 2, 2 * 2 + 1 * 3, 2 * 3 + 1 * 4],
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[2 * 1 + 1 * 2, 2 * 2 + 1 * 3, 2 * 3 + 1 * 4]])
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else:
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self.assertAllClose(
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output,
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[[2 * 1 + 1 * 2, 2 * 3 + 1 * 4], [2 * 1 + 1 * 2, 2 * 3 + 1 * 4]])
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def testConv1DTranspose(self):
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with self.cached_session():
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stride = 2
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# Input, output: [batch, width, depth]
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x_shape = [2, 4, 3]
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y_shape = [2, 9, 2]
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# Filter: [kernel_width, output_depth, input_depth]
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f_shape = [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.conv1d_transpose(
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x, f, y_shape, strides=stride, 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[1]):
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for w in xrange(pad, y_shape[1] - pad):
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target = 3.0
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# We add a case for locations divisible by the stride.
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w_in = w % stride == 0 and w > pad and w < y_shape[1] - 1 - pad
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if w_in:
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target += 3.0
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cache_values[n, 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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self.assertAllClose(cache_values, value)
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if __name__ == "__main__":
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test.main()
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