+++ /dev/null
-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for slim.slim_nets.resnet_v1."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-import tensorflow as tf
-
-from nets import resnet_utils
-from nets import resnet_v1
-
-slim = tf.contrib.slim
-
-
-def create_test_input(batch_size, height, width, channels):
- """Create test input tensor.
-
- Args:
- batch_size: The number of images per batch or `None` if unknown.
- height: The height of each image or `None` if unknown.
- width: The width of each image or `None` if unknown.
- channels: The number of channels per image or `None` if unknown.
-
- Returns:
- Either a placeholder `Tensor` of dimension
- [batch_size, height, width, channels] if any of the inputs are `None` or a
- constant `Tensor` with the mesh grid values along the spatial dimensions.
- """
- if None in [batch_size, height, width, channels]:
- return tf.placeholder(tf.float32, (batch_size, height, width, channels))
- else:
- return tf.to_float(
- np.tile(
- np.reshape(
- np.reshape(np.arange(height), [height, 1]) +
- np.reshape(np.arange(width), [1, width]),
- [1, height, width, 1]),
- [batch_size, 1, 1, channels]))
-
-
-class ResnetUtilsTest(tf.test.TestCase):
-
- def testSubsampleThreeByThree(self):
- x = tf.reshape(tf.to_float(tf.range(9)), [1, 3, 3, 1])
- x = resnet_utils.subsample(x, 2)
- expected = tf.reshape(tf.constant([0, 2, 6, 8]), [1, 2, 2, 1])
- with self.test_session():
- self.assertAllClose(x.eval(), expected.eval())
-
- def testSubsampleFourByFour(self):
- x = tf.reshape(tf.to_float(tf.range(16)), [1, 4, 4, 1])
- x = resnet_utils.subsample(x, 2)
- expected = tf.reshape(tf.constant([0, 2, 8, 10]), [1, 2, 2, 1])
- with self.test_session():
- self.assertAllClose(x.eval(), expected.eval())
-
- def testConv2DSameEven(self):
- n, n2 = 4, 2
-
- # Input image.
- x = create_test_input(1, n, n, 1)
-
- # Convolution kernel.
- w = create_test_input(1, 3, 3, 1)
- w = tf.reshape(w, [3, 3, 1, 1])
-
- tf.get_variable('Conv/weights', initializer=w)
- tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
- tf.get_variable_scope().reuse_variables()
-
- y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
- y1_expected = tf.to_float([[14, 28, 43, 26],
- [28, 48, 66, 37],
- [43, 66, 84, 46],
- [26, 37, 46, 22]])
- y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
-
- y2 = resnet_utils.subsample(y1, 2)
- y2_expected = tf.to_float([[14, 43],
- [43, 84]])
- y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
-
- y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
- y3_expected = y2_expected
-
- y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
- y4_expected = tf.to_float([[48, 37],
- [37, 22]])
- y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1])
-
- with self.test_session() as sess:
- sess.run(tf.global_variables_initializer())
- self.assertAllClose(y1.eval(), y1_expected.eval())
- self.assertAllClose(y2.eval(), y2_expected.eval())
- self.assertAllClose(y3.eval(), y3_expected.eval())
- self.assertAllClose(y4.eval(), y4_expected.eval())
-
- def testConv2DSameOdd(self):
- n, n2 = 5, 3
-
- # Input image.
- x = create_test_input(1, n, n, 1)
-
- # Convolution kernel.
- w = create_test_input(1, 3, 3, 1)
- w = tf.reshape(w, [3, 3, 1, 1])
-
- tf.get_variable('Conv/weights', initializer=w)
- tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
- tf.get_variable_scope().reuse_variables()
-
- y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
- y1_expected = tf.to_float([[14, 28, 43, 58, 34],
- [28, 48, 66, 84, 46],
- [43, 66, 84, 102, 55],
- [58, 84, 102, 120, 64],
- [34, 46, 55, 64, 30]])
- y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
-
- y2 = resnet_utils.subsample(y1, 2)
- y2_expected = tf.to_float([[14, 43, 34],
- [43, 84, 55],
- [34, 55, 30]])
- y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
-
- y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
- y3_expected = y2_expected
-
- y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
- y4_expected = y2_expected
-
- with self.test_session() as sess:
- sess.run(tf.global_variables_initializer())
- self.assertAllClose(y1.eval(), y1_expected.eval())
- self.assertAllClose(y2.eval(), y2_expected.eval())
- self.assertAllClose(y3.eval(), y3_expected.eval())
- self.assertAllClose(y4.eval(), y4_expected.eval())
-
- def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
- """A plain ResNet without extra layers before or after the ResNet blocks."""
- with tf.variable_scope(scope, values=[inputs]):
- with slim.arg_scope([slim.conv2d], outputs_collections='end_points'):
- net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride)
- end_points = slim.utils.convert_collection_to_dict('end_points')
- return net, end_points
-
- def testEndPointsV1(self):
- """Test the end points of a tiny v1 bottleneck network."""
- blocks = [
- resnet_v1.resnet_v1_block(
- 'block1', base_depth=1, num_units=2, stride=2),
- resnet_v1.resnet_v1_block(
- 'block2', base_depth=2, num_units=2, stride=1),
- ]
- inputs = create_test_input(2, 32, 16, 3)
- with slim.arg_scope(resnet_utils.resnet_arg_scope()):
- _, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
- expected = [
- 'tiny/block1/unit_1/bottleneck_v1/shortcut',
- 'tiny/block1/unit_1/bottleneck_v1/conv1',
- 'tiny/block1/unit_1/bottleneck_v1/conv2',
- 'tiny/block1/unit_1/bottleneck_v1/conv3',
- 'tiny/block1/unit_2/bottleneck_v1/conv1',
- 'tiny/block1/unit_2/bottleneck_v1/conv2',
- 'tiny/block1/unit_2/bottleneck_v1/conv3',
- 'tiny/block2/unit_1/bottleneck_v1/shortcut',
- 'tiny/block2/unit_1/bottleneck_v1/conv1',
- 'tiny/block2/unit_1/bottleneck_v1/conv2',
- 'tiny/block2/unit_1/bottleneck_v1/conv3',
- 'tiny/block2/unit_2/bottleneck_v1/conv1',
- 'tiny/block2/unit_2/bottleneck_v1/conv2',
- 'tiny/block2/unit_2/bottleneck_v1/conv3']
- self.assertItemsEqual(expected, end_points)
-
- def _stack_blocks_nondense(self, net, blocks):
- """A simplified ResNet Block stacker without output stride control."""
- for block in blocks:
- with tf.variable_scope(block.scope, 'block', [net]):
- for i, unit in enumerate(block.args):
- with tf.variable_scope('unit_%d' % (i + 1), values=[net]):
- net = block.unit_fn(net, rate=1, **unit)
- return net
-
- def testAtrousValuesBottleneck(self):
- """Verify the values of dense feature extraction by atrous convolution.
-
- Make sure that dense feature extraction by stack_blocks_dense() followed by
- subsampling gives identical results to feature extraction at the nominal
- network output stride using the simple self._stack_blocks_nondense() above.
- """
- block = resnet_v1.resnet_v1_block
- blocks = [
- block('block1', base_depth=1, num_units=2, stride=2),
- block('block2', base_depth=2, num_units=2, stride=2),
- block('block3', base_depth=4, num_units=2, stride=2),
- block('block4', base_depth=8, num_units=2, stride=1),
- ]
- nominal_stride = 8
-
- # Test both odd and even input dimensions.
- height = 30
- width = 31
- with slim.arg_scope(resnet_utils.resnet_arg_scope()):
- with slim.arg_scope([slim.batch_norm], is_training=False):
- for output_stride in [1, 2, 4, 8, None]:
- with tf.Graph().as_default():
- with self.test_session() as sess:
- tf.set_random_seed(0)
- inputs = create_test_input(1, height, width, 3)
- # Dense feature extraction followed by subsampling.
- output = resnet_utils.stack_blocks_dense(inputs,
- blocks,
- output_stride)
- if output_stride is None:
- factor = 1
- else:
- factor = nominal_stride // output_stride
-
- output = resnet_utils.subsample(output, factor)
- # Make the two networks use the same weights.
- tf.get_variable_scope().reuse_variables()
- # Feature extraction at the nominal network rate.
- expected = self._stack_blocks_nondense(inputs, blocks)
- sess.run(tf.global_variables_initializer())
- output, expected = sess.run([output, expected])
- self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
-
-
-class ResnetCompleteNetworkTest(tf.test.TestCase):
- """Tests with complete small ResNet v1 networks."""
-
- def _resnet_small(self,
- inputs,
- num_classes=None,
- is_training=True,
- global_pool=True,
- output_stride=None,
- include_root_block=True,
- reuse=None,
- scope='resnet_v1_small'):
- """A shallow and thin ResNet v1 for faster tests."""
- block = resnet_v1.resnet_v1_block
- blocks = [
- block('block1', base_depth=1, num_units=3, stride=2),
- block('block2', base_depth=2, num_units=3, stride=2),
- block('block3', base_depth=4, num_units=3, stride=2),
- block('block4', base_depth=8, num_units=2, stride=1),
- ]
- return resnet_v1.resnet_v1(inputs, blocks, num_classes,
- is_training=is_training,
- global_pool=global_pool,
- output_stride=output_stride,
- include_root_block=include_root_block,
- reuse=reuse,
- scope=scope)
-
- def testClassificationEndPoints(self):
- global_pool = True
- num_classes = 10
- inputs = create_test_input(2, 224, 224, 3)
- with slim.arg_scope(resnet_utils.resnet_arg_scope()):
- logits, end_points = self._resnet_small(inputs, num_classes,
- global_pool=global_pool,
- scope='resnet')
- self.assertTrue(logits.op.name.startswith('resnet/logits'))
- self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
- self.assertTrue('predictions' in end_points)
- self.assertListEqual(end_points['predictions'].get_shape().as_list(),
- [2, 1, 1, num_classes])
-
- def testClassificationShapes(self):
- global_pool = True
- num_classes = 10
- inputs = create_test_input(2, 224, 224, 3)
- with slim.arg_scope(resnet_utils.resnet_arg_scope()):
- _, end_points = self._resnet_small(inputs, num_classes,
- global_pool=global_pool,
- scope='resnet')
- endpoint_to_shape = {
- 'resnet/block1': [2, 28, 28, 4],
- 'resnet/block2': [2, 14, 14, 8],
- 'resnet/block3': [2, 7, 7, 16],
- 'resnet/block4': [2, 7, 7, 32]}
- for endpoint in endpoint_to_shape:
- shape = endpoint_to_shape[endpoint]
- self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
-
- def testFullyConvolutionalEndpointShapes(self):
- global_pool = False
- num_classes = 10
- inputs = create_test_input(2, 321, 321, 3)
- with slim.arg_scope(resnet_utils.resnet_arg_scope()):
- _, end_points = self._resnet_small(inputs, num_classes,
- global_pool=global_pool,
- scope='resnet')
- endpoint_to_shape = {
- 'resnet/block1': [2, 41, 41, 4],
- 'resnet/block2': [2, 21, 21, 8],
- 'resnet/block3': [2, 11, 11, 16],
- 'resnet/block4': [2, 11, 11, 32]}
- for endpoint in endpoint_to_shape:
- shape = endpoint_to_shape[endpoint]
- self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
-
- def testRootlessFullyConvolutionalEndpointShapes(self):
- global_pool = False
- num_classes = 10
- inputs = create_test_input(2, 128, 128, 3)
- with slim.arg_scope(resnet_utils.resnet_arg_scope()):
- _, end_points = self._resnet_small(inputs, num_classes,
- global_pool=global_pool,
- include_root_block=False,
- scope='resnet')
- endpoint_to_shape = {
- 'resnet/block1': [2, 64, 64, 4],
- 'resnet/block2': [2, 32, 32, 8],
- 'resnet/block3': [2, 16, 16, 16],
- 'resnet/block4': [2, 16, 16, 32]}
- for endpoint in endpoint_to_shape:
- shape = endpoint_to_shape[endpoint]
- self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
-
- def testAtrousFullyConvolutionalEndpointShapes(self):
- global_pool = False
- num_classes = 10
- output_stride = 8
- inputs = create_test_input(2, 321, 321, 3)
- with slim.arg_scope(resnet_utils.resnet_arg_scope()):
- _, end_points = self._resnet_small(inputs,
- num_classes,
- global_pool=global_pool,
- output_stride=output_stride,
- scope='resnet')
- endpoint_to_shape = {
- 'resnet/block1': [2, 41, 41, 4],
- 'resnet/block2': [2, 41, 41, 8],
- 'resnet/block3': [2, 41, 41, 16],
- 'resnet/block4': [2, 41, 41, 32]}
- for endpoint in endpoint_to_shape:
- shape = endpoint_to_shape[endpoint]
- self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
-
- def testAtrousFullyConvolutionalValues(self):
- """Verify dense feature extraction with atrous convolution."""
- nominal_stride = 32
- for output_stride in [4, 8, 16, 32, None]:
- with slim.arg_scope(resnet_utils.resnet_arg_scope()):
- with tf.Graph().as_default():
- with self.test_session() as sess:
- tf.set_random_seed(0)
- inputs = create_test_input(2, 81, 81, 3)
- # Dense feature extraction followed by subsampling.
- output, _ = self._resnet_small(inputs, None, is_training=False,
- global_pool=False,
- output_stride=output_stride)
- if output_stride is None:
- factor = 1
- else:
- factor = nominal_stride // output_stride
- output = resnet_utils.subsample(output, factor)
- # Make the two networks use the same weights.
- tf.get_variable_scope().reuse_variables()
- # Feature extraction at the nominal network rate.
- expected, _ = self._resnet_small(inputs, None, is_training=False,
- global_pool=False)
- sess.run(tf.global_variables_initializer())
- self.assertAllClose(output.eval(), expected.eval(),
- atol=1e-4, rtol=1e-4)
-
- def testUnknownBatchSize(self):
- batch = 2
- height, width = 65, 65
- global_pool = True
- num_classes = 10
- inputs = create_test_input(None, height, width, 3)
- with slim.arg_scope(resnet_utils.resnet_arg_scope()):
- logits, _ = self._resnet_small(inputs, num_classes,
- global_pool=global_pool,
- scope='resnet')
- self.assertTrue(logits.op.name.startswith('resnet/logits'))
- self.assertListEqual(logits.get_shape().as_list(),
- [None, 1, 1, num_classes])
- images = create_test_input(batch, height, width, 3)
- with self.test_session() as sess:
- sess.run(tf.global_variables_initializer())
- output = sess.run(logits, {inputs: images.eval()})
- self.assertEqual(output.shape, (batch, 1, 1, num_classes))
-
- def testFullyConvolutionalUnknownHeightWidth(self):
- batch = 2
- height, width = 65, 65
- global_pool = False
- inputs = create_test_input(batch, None, None, 3)
- with slim.arg_scope(resnet_utils.resnet_arg_scope()):
- output, _ = self._resnet_small(inputs, None, global_pool=global_pool)
- self.assertListEqual(output.get_shape().as_list(),
- [batch, None, None, 32])
- images = create_test_input(batch, height, width, 3)
- with self.test_session() as sess:
- sess.run(tf.global_variables_initializer())
- output = sess.run(output, {inputs: images.eval()})
- self.assertEqual(output.shape, (batch, 3, 3, 32))
-
- def testAtrousFullyConvolutionalUnknownHeightWidth(self):
- batch = 2
- height, width = 65, 65
- global_pool = False
- output_stride = 8
- inputs = create_test_input(batch, None, None, 3)
- with slim.arg_scope(resnet_utils.resnet_arg_scope()):
- output, _ = self._resnet_small(inputs,
- None,
- global_pool=global_pool,
- output_stride=output_stride)
- self.assertListEqual(output.get_shape().as_list(),
- [batch, None, None, 32])
- images = create_test_input(batch, height, width, 3)
- with self.test_session() as sess:
- sess.run(tf.global_variables_initializer())
- output = sess.run(output, {inputs: images.eval()})
- self.assertEqual(output.shape, (batch, 9, 9, 32))
-
-
-if __name__ == '__main__':
- tf.test.main()
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