def testAtrousFullyConvolutionalValues(self): """Verify dense feature extraction with atrous convolution.""" nominal_stride = 32 for output_stride in [4, 8, 16, 32, None]: with arg_scope(resnet_utils.resnet_arg_scope()): with ops.Graph().as_default(): with self.cached_session() as sess: random_seed.set_random_seed(0) inputs = create_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. variable_scope.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(variables.global_variables_initializer()) self.assertAllClose(output.eval(), expected.eval(), atol=2e-4, rtol=1e-4)
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_input(2, 32, 16, 3) with 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 _build_tail(self, inputs, is_training=False): if not self._use_tail: return inputs if self._architecture == "resnet_v1_101": train_batch_norm = is_training and self._config.get("train_batch_norm") with self._enter_variable_scope(): weight_decay = self._config.get("arg_scope", {}).get("weight_decay", 0) with tf.variable_scope(self._architecture, reuse=True): resnet_arg_scope = resnet_utils.resnet_arg_scope( batch_norm_epsilon=1e-5, batch_norm_scale=True, weight_decay=weight_decay ) with slim.arg_scope(resnet_arg_scope): with slim.arg_scope([slim.batch_norm], is_training=train_batch_norm): blocks = [ resnet_utils.Block( "block4", resnet_v1.bottleneck, [{"depth": 2048, "depth_bottleneck": 512, "stride": 1}] * 3, ) ] proposal_classifier_features = resnet_utils.stack_blocks_dense(inputs, blocks) else: proposal_classifier_features = inputs return proposal_classifier_features
def testClassificationEndPoints(self): global_pool = True num_classes = 10 inputs = create_input(2, 224, 224, 3) with 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 testFullyConvolutionalUnknownHeightWidth(self): batch = 2 height, width = 65, 65 global_pool = False inputs = create_input(batch, None, None, 3) with 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_input(batch, height, width, 3) with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) output = sess.run(output, {inputs: images.eval()}) self.assertEqual(output.shape, (batch, 3, 3, 32))
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 arg_scope(resnet_utils.resnet_arg_scope()): with arg_scope([layers.batch_norm], is_training=False): for output_stride in [1, 2, 4, 8, None]: with ops.Graph().as_default(): with self.cached_session() as sess: random_seed.set_random_seed(0) inputs = create_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. variable_scope.get_variable_scope( ).reuse_variables() # Feature extraction at the nominal network rate. expected = self._stack_blocks_nondense( inputs, blocks) sess.run(variables.global_variables_initializer()) output, expected = sess.run([output, expected]) self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
def testUnknownBatchSize(self): batch = 2 height, width = 65, 65 global_pool = True num_classes = 10 inputs = create_input(None, height, width, 3) with 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_input(batch, height, width, 3) with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) output = sess.run(logits, {inputs: images.eval()}) self.assertEqual(output.shape, (batch, 1, 1, num_classes))
def testFullyConvolutionalEndpointShapes(self): global_pool = False num_classes = 10 inputs = create_input(2, 321, 321, 3) with 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 testClassificationShapes(self): global_pool = True num_classes = 10 inputs = create_input(2, 224, 224, 3) with 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 _build_tail(self, inputs, is_training=False): if not self._use_tail: return inputs if self._architecture == 'resnet_v1_101': train_batch_norm = ( is_training and self._config.get('train_batch_norm') ) with self._enter_variable_scope(): weight_decay = ( self._config.get('arg_scope', {}).get('weight_decay', 0) ) with tf.variable_scope(self._architecture, reuse=True): resnet_arg_scope = resnet_utils.resnet_arg_scope( batch_norm_epsilon=1e-5, batch_norm_scale=True, weight_decay=weight_decay ) with slim.arg_scope(resnet_arg_scope): with slim.arg_scope( [slim.batch_norm], is_training=train_batch_norm ): blocks = [ resnet_utils.Block( 'block4', resnet_v1.bottleneck, [{ 'depth': 2048, 'depth_bottleneck': 512, 'stride': 1 }] * 3 ) ] proposal_classifier_features = ( resnet_utils.stack_blocks_dense(inputs, blocks) ) else: proposal_classifier_features = inputs return proposal_classifier_features