454 lines
19 KiB
Python
454 lines
19 KiB
Python
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# Copyright 2016 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 slim.nets.resnet_v2."""
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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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import tensorflow as tf
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from nets import resnet_utils
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from nets import resnet_v2
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slim = tf.contrib.slim
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def create_test_input(batch_size, height, width, channels):
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"""Create test input tensor.
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Args:
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batch_size: The number of images per batch or `None` if unknown.
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height: The height of each image or `None` if unknown.
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width: The width of each image or `None` if unknown.
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channels: The number of channels per image or `None` if unknown.
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Returns:
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Either a placeholder `Tensor` of dimension
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[batch_size, height, width, channels] if any of the inputs are `None` or a
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constant `Tensor` with the mesh grid values along the spatial dimensions.
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"""
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if None in [batch_size, height, width, channels]:
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return tf.placeholder(tf.float32, (batch_size, height, width, channels))
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else:
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return tf.to_float(
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np.tile(
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np.reshape(
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np.reshape(np.arange(height), [height, 1]) +
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np.reshape(np.arange(width), [1, width]),
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[1, height, width, 1]),
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[batch_size, 1, 1, channels]))
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class ResnetUtilsTest(tf.test.TestCase):
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def testSubsampleThreeByThree(self):
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x = tf.reshape(tf.to_float(tf.range(9)), [1, 3, 3, 1])
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x = resnet_utils.subsample(x, 2)
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expected = tf.reshape(tf.constant([0, 2, 6, 8]), [1, 2, 2, 1])
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with self.test_session():
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self.assertAllClose(x.eval(), expected.eval())
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def testSubsampleFourByFour(self):
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x = tf.reshape(tf.to_float(tf.range(16)), [1, 4, 4, 1])
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x = resnet_utils.subsample(x, 2)
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expected = tf.reshape(tf.constant([0, 2, 8, 10]), [1, 2, 2, 1])
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with self.test_session():
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self.assertAllClose(x.eval(), expected.eval())
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def testConv2DSameEven(self):
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n, n2 = 4, 2
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# Input image.
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x = create_test_input(1, n, n, 1)
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# Convolution kernel.
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w = create_test_input(1, 3, 3, 1)
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w = tf.reshape(w, [3, 3, 1, 1])
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tf.get_variable('Conv/weights', initializer=w)
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tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
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tf.get_variable_scope().reuse_variables()
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y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
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y1_expected = tf.to_float([[14, 28, 43, 26],
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[28, 48, 66, 37],
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[43, 66, 84, 46],
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[26, 37, 46, 22]])
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y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
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y2 = resnet_utils.subsample(y1, 2)
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y2_expected = tf.to_float([[14, 43],
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[43, 84]])
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y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
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y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
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y3_expected = y2_expected
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y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
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y4_expected = tf.to_float([[48, 37],
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[37, 22]])
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y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1])
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with self.test_session() as sess:
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sess.run(tf.initialize_all_variables())
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self.assertAllClose(y1.eval(), y1_expected.eval())
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self.assertAllClose(y2.eval(), y2_expected.eval())
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self.assertAllClose(y3.eval(), y3_expected.eval())
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self.assertAllClose(y4.eval(), y4_expected.eval())
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def testConv2DSameOdd(self):
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n, n2 = 5, 3
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# Input image.
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x = create_test_input(1, n, n, 1)
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# Convolution kernel.
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w = create_test_input(1, 3, 3, 1)
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w = tf.reshape(w, [3, 3, 1, 1])
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tf.get_variable('Conv/weights', initializer=w)
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tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
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tf.get_variable_scope().reuse_variables()
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y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
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y1_expected = tf.to_float([[14, 28, 43, 58, 34],
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[28, 48, 66, 84, 46],
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[43, 66, 84, 102, 55],
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[58, 84, 102, 120, 64],
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[34, 46, 55, 64, 30]])
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y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
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y2 = resnet_utils.subsample(y1, 2)
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y2_expected = tf.to_float([[14, 43, 34],
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[43, 84, 55],
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[34, 55, 30]])
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y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
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y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
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y3_expected = y2_expected
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y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
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y4_expected = y2_expected
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with self.test_session() as sess:
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sess.run(tf.initialize_all_variables())
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self.assertAllClose(y1.eval(), y1_expected.eval())
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self.assertAllClose(y2.eval(), y2_expected.eval())
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self.assertAllClose(y3.eval(), y3_expected.eval())
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self.assertAllClose(y4.eval(), y4_expected.eval())
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def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None):
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"""A plain ResNet without extra layers before or after the ResNet blocks."""
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with tf.variable_scope(scope, values=[inputs]):
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with slim.arg_scope([slim.conv2d], outputs_collections='end_points'):
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net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride)
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end_points = dict(tf.get_collection('end_points'))
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return net, end_points
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def testEndPointsV2(self):
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"""Test the end points of a tiny v2 bottleneck network."""
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bottleneck = resnet_v2.bottleneck
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blocks = [resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
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resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 1)])]
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inputs = create_test_input(2, 32, 16, 3)
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with slim.arg_scope(resnet_utils.resnet_arg_scope()):
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_, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
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expected = [
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'tiny/block1/unit_1/bottleneck_v2/shortcut',
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'tiny/block1/unit_1/bottleneck_v2/conv1',
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'tiny/block1/unit_1/bottleneck_v2/conv2',
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'tiny/block1/unit_1/bottleneck_v2/conv3',
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'tiny/block1/unit_2/bottleneck_v2/conv1',
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'tiny/block1/unit_2/bottleneck_v2/conv2',
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'tiny/block1/unit_2/bottleneck_v2/conv3',
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'tiny/block2/unit_1/bottleneck_v2/shortcut',
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'tiny/block2/unit_1/bottleneck_v2/conv1',
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'tiny/block2/unit_1/bottleneck_v2/conv2',
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'tiny/block2/unit_1/bottleneck_v2/conv3',
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'tiny/block2/unit_2/bottleneck_v2/conv1',
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'tiny/block2/unit_2/bottleneck_v2/conv2',
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'tiny/block2/unit_2/bottleneck_v2/conv3']
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self.assertItemsEqual(expected, end_points)
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def _stack_blocks_nondense(self, net, blocks):
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"""A simplified ResNet Block stacker without output stride control."""
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for block in blocks:
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with tf.variable_scope(block.scope, 'block', [net]):
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for i, unit in enumerate(block.args):
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depth, depth_bottleneck, stride = unit
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with tf.variable_scope('unit_%d' % (i + 1), values=[net]):
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net = block.unit_fn(net,
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depth=depth,
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depth_bottleneck=depth_bottleneck,
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stride=stride,
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rate=1)
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return net
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def _atrousValues(self, bottleneck):
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"""Verify the values of dense feature extraction by atrous convolution.
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Make sure that dense feature extraction by stack_blocks_dense() followed by
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subsampling gives identical results to feature extraction at the nominal
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network output stride using the simple self._stack_blocks_nondense() above.
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Args:
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bottleneck: The bottleneck function.
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"""
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blocks = [
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resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
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resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]),
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resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]),
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resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)])
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]
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nominal_stride = 8
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# Test both odd and even input dimensions.
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height = 30
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width = 31
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with slim.arg_scope(resnet_utils.resnet_arg_scope()):
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with slim.arg_scope([slim.batch_norm], is_training=False):
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for output_stride in [1, 2, 4, 8, None]:
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with tf.Graph().as_default():
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with self.test_session() as sess:
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tf.set_random_seed(0)
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inputs = create_test_input(1, height, width, 3)
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# Dense feature extraction followed by subsampling.
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output = resnet_utils.stack_blocks_dense(inputs,
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blocks,
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output_stride)
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if output_stride is None:
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factor = 1
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else:
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factor = nominal_stride // output_stride
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output = resnet_utils.subsample(output, factor)
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# Make the two networks use the same weights.
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tf.get_variable_scope().reuse_variables()
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# Feature extraction at the nominal network rate.
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expected = self._stack_blocks_nondense(inputs, blocks)
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sess.run(tf.initialize_all_variables())
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output, expected = sess.run([output, expected])
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self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
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def testAtrousValuesBottleneck(self):
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self._atrousValues(resnet_v2.bottleneck)
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class ResnetCompleteNetworkTest(tf.test.TestCase):
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"""Tests with complete small ResNet v2 networks."""
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def _resnet_small(self,
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inputs,
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num_classes=None,
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is_training=True,
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global_pool=True,
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output_stride=None,
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include_root_block=True,
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reuse=None,
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scope='resnet_v2_small'):
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"""A shallow and thin ResNet v2 for faster tests."""
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bottleneck = resnet_v2.bottleneck
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blocks = [
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resnet_utils.Block(
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'block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]),
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resnet_utils.Block(
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'block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]),
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resnet_utils.Block(
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'block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]),
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resnet_utils.Block(
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'block4', bottleneck, [(32, 8, 1)] * 2)]
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return resnet_v2.resnet_v2(inputs, blocks, num_classes,
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is_training=is_training,
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global_pool=global_pool,
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output_stride=output_stride,
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include_root_block=include_root_block,
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reuse=reuse,
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scope=scope)
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def testClassificationEndPoints(self):
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global_pool = True
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num_classes = 10
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inputs = create_test_input(2, 224, 224, 3)
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with slim.arg_scope(resnet_utils.resnet_arg_scope()):
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logits, end_points = self._resnet_small(inputs, num_classes,
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global_pool=global_pool,
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scope='resnet')
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self.assertTrue(logits.op.name.startswith('resnet/logits'))
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self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
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self.assertTrue('predictions' in end_points)
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self.assertListEqual(end_points['predictions'].get_shape().as_list(),
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[2, 1, 1, num_classes])
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def testClassificationShapes(self):
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global_pool = True
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num_classes = 10
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inputs = create_test_input(2, 224, 224, 3)
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with slim.arg_scope(resnet_utils.resnet_arg_scope()):
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_, end_points = self._resnet_small(inputs, num_classes,
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global_pool=global_pool,
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scope='resnet')
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endpoint_to_shape = {
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'resnet/block1': [2, 28, 28, 4],
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'resnet/block2': [2, 14, 14, 8],
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'resnet/block3': [2, 7, 7, 16],
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'resnet/block4': [2, 7, 7, 32]}
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for endpoint in endpoint_to_shape:
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shape = endpoint_to_shape[endpoint]
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self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
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def testFullyConvolutionalEndpointShapes(self):
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global_pool = False
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num_classes = 10
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inputs = create_test_input(2, 321, 321, 3)
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with slim.arg_scope(resnet_utils.resnet_arg_scope()):
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_, end_points = self._resnet_small(inputs, num_classes,
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global_pool=global_pool,
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scope='resnet')
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endpoint_to_shape = {
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'resnet/block1': [2, 41, 41, 4],
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'resnet/block2': [2, 21, 21, 8],
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'resnet/block3': [2, 11, 11, 16],
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'resnet/block4': [2, 11, 11, 32]}
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for endpoint in endpoint_to_shape:
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|
shape = endpoint_to_shape[endpoint]
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|
self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
|
||
|
|
|
||
|
|
def testRootlessFullyConvolutionalEndpointShapes(self):
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||
|
|
global_pool = False
|
||
|
|
num_classes = 10
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||
|
|
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.initialize_all_variables())
|
||
|
|
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.initialize_all_variables())
|
||
|
|
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.initialize_all_variables())
|
||
|
|
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.initialize_all_variables())
|
||
|
|
output = sess.run(output, {inputs: images.eval()})
|
||
|
|
self.assertEqual(output.shape, (batch, 9, 9, 32))
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
tf.test.main()
|