def testModelHasExpectedNumberOfParameters(self): batch_size = 5 height, width = 299, 299 inputs = random_ops.random_uniform((batch_size, height, width, 3)) with arg_scope(inception_v3.inception_v3_arg_scope()): inception_v3.inception_v3_base(inputs) total_params, _ = model_analyzer.analyze_vars( variables_lib.get_model_variables()) self.assertAlmostEqual(21802784, total_params)
def testBuildAndCheckAllEndPointsUptoMixed7c(self): batch_size = 5 height, width = 299, 299 inputs = random_ops.random_uniform((batch_size, height, width, 3)) _, end_points = inception_v3.inception_v3_base( inputs, final_endpoint='Mixed_7c') endpoints_shapes = { 'Conv2d_1a_3x3': [batch_size, 149, 149, 32], 'Conv2d_2a_3x3': [batch_size, 147, 147, 32], 'Conv2d_2b_3x3': [batch_size, 147, 147, 64], 'MaxPool_3a_3x3': [batch_size, 73, 73, 64], 'Conv2d_3b_1x1': [batch_size, 73, 73, 80], 'Conv2d_4a_3x3': [batch_size, 71, 71, 192], 'MaxPool_5a_3x3': [batch_size, 35, 35, 192], 'Mixed_5b': [batch_size, 35, 35, 256], 'Mixed_5c': [batch_size, 35, 35, 288], 'Mixed_5d': [batch_size, 35, 35, 288], 'Mixed_6a': [batch_size, 17, 17, 768], 'Mixed_6b': [batch_size, 17, 17, 768], 'Mixed_6c': [batch_size, 17, 17, 768], 'Mixed_6d': [batch_size, 17, 17, 768], 'Mixed_6e': [batch_size, 17, 17, 768], 'Mixed_7a': [batch_size, 8, 8, 1280], 'Mixed_7b': [batch_size, 8, 8, 2048], 'Mixed_7c': [batch_size, 8, 8, 2048] } self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) for endpoint_name in endpoints_shapes: expected_shape = endpoints_shapes[endpoint_name] self.assertTrue(endpoint_name in end_points) self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), expected_shape)
def testBuildBaseNetwork(self): batch_size = 5 height, width = 299, 299 inputs = random_ops.random_uniform((batch_size, height, width, 3)) final_endpoint, end_points = inception_v3.inception_v3_base(inputs) self.assertTrue(final_endpoint.op.name.startswith('InceptionV3/Mixed_7c')) self.assertListEqual(final_endpoint.get_shape().as_list(), [batch_size, 8, 8, 2048]) expected_endpoints = [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c' ] self.assertItemsEqual(end_points.keys(), expected_endpoints)
def testBuildOnlyUptoFinalEndpoint(self): batch_size = 5 height, width = 299, 299 endpoints = [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c' ] for index, endpoint in enumerate(endpoints): with ops.Graph().as_default(): inputs = random_ops.random_uniform((batch_size, height, width, 3)) out_tensor, end_points = inception_v3.inception_v3_base( inputs, final_endpoint=endpoint) self.assertTrue( out_tensor.op.name.startswith('InceptionV3/' + endpoint)) self.assertItemsEqual(endpoints[:index + 1], end_points)
def extract_features(self, preprocessed_inputs): """Extract features from preprocessed inputs. Args: preprocessed_inputs: a [batch, height, width, channels] float tensor representing a batch of images. Returns: feature_maps: a list of tensors where the ith tensor has shape [batch, height_i, width_i, depth_i] """ preprocessed_inputs.get_shape().assert_has_rank(4) shape_assert = tf.Assert( tf.logical_and( tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33), tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)), ['image size must at least be 33 in both height and width.']) feature_map_layout = { 'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''], 'layer_depth': [-1, -1, -1, 512, 256, 128], } with tf.control_dependencies([shape_assert]): with slim.arg_scope(self._conv_hyperparams): with tf.variable_scope('InceptionV3', reuse=self._reuse_weights) as scope: _, image_features = inception_v3.inception_v3_base( ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple), final_endpoint='Mixed_7c', min_depth=self._min_depth, depth_multiplier=self._depth_multiplier, scope=scope) feature_maps = feature_map_generators.multi_resolution_feature_maps( feature_map_layout=feature_map_layout, depth_multiplier=self._depth_multiplier, min_depth=self._min_depth, insert_1x1_conv=True, image_features=image_features) return feature_maps.values()
def extract_features(self, preprocessed_inputs): """Extract features from preprocessed inputs. Args: preprocessed_inputs: a [batch, height, width, channels] float tensor representing a batch of images. Returns: feature_maps: a list of tensors where the ith tensor has shape [batch, height_i, width_i, depth_i] """ preprocessed_inputs = shape_utils.check_min_image_dim( 33, preprocessed_inputs) feature_map_layout = { 'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''][:self._num_layers], 'layer_depth': [-1, -1, -1, 512, 256, 128][:self._num_layers], 'use_explicit_padding': self._use_explicit_padding, 'use_depthwise': self._use_depthwise, } with slim.arg_scope(self._conv_hyperparams_fn()): with tf.variable_scope('InceptionV3', reuse=self._reuse_weights) as scope: _, image_features = inception_v3.inception_v3_base( ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple), final_endpoint='Mixed_7c', min_depth=self._min_depth, depth_multiplier=self._depth_multiplier, scope=scope) feature_maps = feature_map_generators.multi_resolution_feature_maps( feature_map_layout=feature_map_layout, depth_multiplier=self._depth_multiplier, min_depth=self._min_depth, insert_1x1_conv=True, image_features=image_features) return feature_maps.values()