def testModelHasExpectedNumberOfParameters(self): batch_size = 5 height, width = 224, 224 inputs = tf.random_uniform((batch_size, height, width, 3)) with slim.arg_scope(inception.inception_v2_arg_scope()): inception.inception_v2_base(inputs) total_params, _ = slim.model_analyzer.analyze_vars( slim.get_model_variables()) self.assertAlmostEqual(10173112, total_params)
def testBuildErrorsForDataFormats(self): batch_size = 5 height, width = 224, 224 inputs = tf.random_uniform((batch_size, height, width, 3)) # 'NCWH' data format is not supported. with self.assertRaises(ValueError): _ = inception.inception_v2_base(inputs, data_format='NCWH') # 'NCHW' data format is not supported for separable convolution. with self.assertRaises(ValueError): _ = inception.inception_v2_base(inputs, data_format='NCHW')
def testBuildAndCheckAllEndPointsUptoMixed5c(self): batch_size = 5 height, width = 224, 224 inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v2_base(inputs, final_endpoint='Mixed_5c') endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256], 'Mixed_3c': [batch_size, 28, 28, 320], 'Mixed_4a': [batch_size, 14, 14, 576], 'Mixed_4b': [batch_size, 14, 14, 576], 'Mixed_4c': [batch_size, 14, 14, 576], 'Mixed_4d': [batch_size, 14, 14, 576], 'Mixed_4e': [batch_size, 14, 14, 576], 'Mixed_5a': [batch_size, 7, 7, 1024], 'Mixed_5b': [batch_size, 7, 7, 1024], 'Mixed_5c': [batch_size, 7, 7, 1024], 'Conv2d_1a_7x7': [batch_size, 112, 112, 64], 'MaxPool_2a_3x3': [batch_size, 56, 56, 64], 'Conv2d_2b_1x1': [batch_size, 56, 56, 64], 'Conv2d_2c_3x3': [batch_size, 56, 56, 192], 'MaxPool_3a_3x3': [batch_size, 28, 28, 192]} 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 testBuildBaseNetworkWithoutRootBlock(self): batch_size = 5 height, width = 28, 28 channels = 192 inputs = tf.random_uniform((batch_size, height, width, channels)) _, end_points = inception.inception_v2_base(inputs, include_root_block=False) endpoints_shapes = { 'Mixed_3b': [batch_size, 28, 28, 256], 'Mixed_3c': [batch_size, 28, 28, 320], 'Mixed_4a': [batch_size, 14, 14, 576], 'Mixed_4b': [batch_size, 14, 14, 576], 'Mixed_4c': [batch_size, 14, 14, 576], 'Mixed_4d': [batch_size, 14, 14, 576], 'Mixed_4e': [batch_size, 14, 14, 576], 'Mixed_5a': [batch_size, 7, 7, 1024], 'Mixed_5b': [batch_size, 7, 7, 1024], 'Mixed_5c': [batch_size, 7, 7, 1024] } 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 testBuildEndPointsWithUseSeparableConvolutionFalse(self): batch_size = 5 height, width = 224, 224 inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v2_base(inputs) endpoint_keys = [ key for key in end_points.keys() if key.startswith('Mixed') or key.startswith('Conv') ] _, end_points_with_replacement = inception.inception_v2_base( inputs, use_separable_conv=False) # The endpoint shapes must be equal to the original shape even when the # separable convolution is replaced with a normal convolution. for key in endpoint_keys: original_shape = end_points[key].get_shape().as_list() self.assertTrue(key in end_points_with_replacement) new_shape = end_points_with_replacement[key].get_shape().as_list() self.assertListEqual(original_shape, new_shape)
def testBuildOnlyUptoFinalEndpoint(self): batch_size = 5 height, width = 224, 224 endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c'] for index, endpoint in enumerate(endpoints): with tf.Graph().as_default(): inputs = tf.random_uniform((batch_size, height, width, 3)) out_tensor, end_points = inception.inception_v2_base( inputs, final_endpoint=endpoint) self.assertTrue(out_tensor.op.name.startswith( 'InceptionV2/' + endpoint)) self.assertItemsEqual(endpoints[:index+1], end_points)
def testBuildBaseNetwork(self): batch_size = 5 height, width = 224, 224 inputs = tf.random_uniform((batch_size, height, width, 3)) mixed_5c, end_points = inception.inception_v2_base(inputs) self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c')) self.assertListEqual(mixed_5c.get_shape().as_list(), [batch_size, 7, 7, 1024]) expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3', 'MaxPool_3a_3x3'] self.assertItemsEqual(end_points.keys(), expected_endpoints)
def testBuildEndPointsNCHWDataFormat(self): batch_size = 5 height, width = 224, 224 inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v2_base(inputs) endpoint_keys = [ key for key in end_points.keys() if key.startswith('Mixed') or key.startswith('Conv') ] inputs_in_nchw = tf.random_uniform((batch_size, 3, height, width)) _, end_points_with_replacement = inception.inception_v2_base( inputs_in_nchw, use_separable_conv=False, data_format='NCHW') # With the 'NCHW' data format, all endpoint activations have a transposed # shape from the original shape with the 'NHWC' layout. for key in endpoint_keys: transposed_original_shape = tf.transpose( end_points[key], [0, 3, 1, 2]).get_shape().as_list() self.assertTrue(key in end_points_with_replacement) new_shape = end_points_with_replacement[key].get_shape().as_list() self.assertListEqual(transposed_original_shape, new_shape)
def _construct_model(model_type='resnet_v1_50'): """Constructs model for the desired type of CNN. Args: model_type: Type of model to be used. Returns: end_points: A dictionary from components of the network to the corresponding activations. Raises: ValueError: If the model_type is not supported. """ # Placeholder input. images = array_ops.placeholder( dtypes.float32, shape=(1, None, None, 3), name=_INPUT_NODE) # Construct model. if model_type == 'inception_resnet_v2': _, end_points = inception.inception_resnet_v2_base(images) elif model_type == 'inception_resnet_v2-same': _, end_points = inception.inception_resnet_v2_base( images, align_feature_maps=True) elif model_type == 'inception_v2': _, end_points = inception.inception_v2_base(images) elif model_type == 'inception_v2-no-separable-conv': _, end_points = inception.inception_v2_base( images, use_separable_conv=False) elif model_type == 'inception_v3': _, end_points = inception.inception_v3_base(images) elif model_type == 'inception_v4': _, end_points = inception.inception_v4_base(images) elif model_type == 'alexnet_v2': _, end_points = alexnet.alexnet_v2(images) elif model_type == 'vgg_a': _, end_points = vgg.vgg_a(images) elif model_type == 'vgg_16': _, end_points = vgg.vgg_16(images) elif model_type == 'mobilenet_v1': _, end_points = mobilenet_v1.mobilenet_v1_base(images) elif model_type == 'mobilenet_v1_075': _, end_points = mobilenet_v1.mobilenet_v1_base( images, depth_multiplier=0.75) elif model_type == 'resnet_v1_50': _, end_points = resnet_v1.resnet_v1_50( images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v1_101': _, end_points = resnet_v1.resnet_v1_101( images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v1_152': _, end_points = resnet_v1.resnet_v1_152( images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v1_200': _, end_points = resnet_v1.resnet_v1_200( images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v2_50': _, end_points = resnet_v2.resnet_v2_50( images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v2_101': _, end_points = resnet_v2.resnet_v2_101( images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v2_152': _, end_points = resnet_v2.resnet_v2_152( images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v2_200': _, end_points = resnet_v2.resnet_v2_200( images, num_classes=None, is_training=False, global_pool=False) else: raise ValueError('Unsupported model_type %s.' % model_type) return end_points
def _construct_model(model_type='resnet_v1_50'): """Constructs model for the desired type of CNN. Args: model_type: Type of model to be used. Returns: end_points: A dictionary from components of the network to the corresponding activations. Raises: ValueError: If the model_type is not supported. """ # Placeholder input. images = array_ops.placeholder(dtypes.float32, shape=(1, None, None, 3), name=_INPUT_NODE) # Construct model. if model_type == 'inception_resnet_v2': _, end_points = inception.inception_resnet_v2_base(images) elif model_type == 'inception_resnet_v2-same': _, end_points = inception.inception_resnet_v2_base( images, align_feature_maps=True) elif model_type == 'inception_v2': _, end_points = inception.inception_v2_base(images) elif model_type == 'inception_v2-no-separable-conv': _, end_points = inception.inception_v2_base(images, use_separable_conv=False) elif model_type == 'inception_v3': _, end_points = inception.inception_v3_base(images) elif model_type == 'inception_v4': _, end_points = inception.inception_v4_base(images) elif model_type == 'alexnet_v2': _, end_points = alexnet.alexnet_v2(images) elif model_type == 'vgg_a': _, end_points = vgg.vgg_a(images) elif model_type == 'vgg_16': _, end_points = vgg.vgg_16(images) elif model_type == 'mobilenet_v1': _, end_points = mobilenet_v1.mobilenet_v1_base(images) elif model_type == 'mobilenet_v1_075': _, end_points = mobilenet_v1.mobilenet_v1_base(images, depth_multiplier=0.75) elif model_type == 'resnet_v1_50': _, end_points = resnet_v1.resnet_v1_50(images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v1_101': _, end_points = resnet_v1.resnet_v1_101(images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v1_152': _, end_points = resnet_v1.resnet_v1_152(images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v1_200': _, end_points = resnet_v1.resnet_v1_200(images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v2_50': _, end_points = resnet_v2.resnet_v2_50(images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v2_101': _, end_points = resnet_v2.resnet_v2_101(images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v2_152': _, end_points = resnet_v2.resnet_v2_152(images, num_classes=None, is_training=False, global_pool=False) elif model_type == 'resnet_v2_200': _, end_points = resnet_v2.resnet_v2_200(images, num_classes=None, is_training=False, global_pool=False) else: raise ValueError('Unsupported model_type %s.' % model_type) return end_points