def test_resnet3d_network_creation(self, model_id, temporal_size, spatial_size, activation, aggregate_endpoints): """Test for creation of a ResNet3D-50 classifier.""" input_specs = tf.keras.layers.InputSpec( shape=[None, temporal_size, spatial_size, spatial_size, 3]) temporal_strides = [1, 1, 1, 1] temporal_kernel_sizes = [(3, 3, 3), (3, 1, 3, 1), (3, 1, 3, 1, 3, 1), (1, 3, 1)] tf.keras.backend.set_image_data_format('channels_last') backbone = backbones.ResNet3D( model_id=model_id, temporal_strides=temporal_strides, temporal_kernel_sizes=temporal_kernel_sizes, input_specs=input_specs, activation=activation) num_classes = 1000 model = video_classification_model.VideoClassificationModel( backbone=backbone, num_classes=num_classes, input_specs={'image': input_specs}, dropout_rate=0.2, aggregate_endpoints=aggregate_endpoints, ) inputs = np.random.rand(2, temporal_size, spatial_size, spatial_size, 3) logits = model(inputs) self.assertAllEqual([2, num_classes], logits.numpy().shape)
def test_resnet_3d_creation(self, model_type): """Test creation of ResNet 3D models.""" backbone_cfg = backbones_3d_cfg.Backbone3D(type=model_type).get() temporal_strides = [] temporal_kernel_sizes = [] for block_spec in backbone_cfg.block_specs: temporal_strides.append(block_spec.temporal_strides) temporal_kernel_sizes.append(block_spec.temporal_kernel_sizes) _ = backbones.ResNet3D(model_id=backbone_cfg.model_id, temporal_strides=temporal_strides, temporal_kernel_sizes=temporal_kernel_sizes, norm_momentum=0.99, norm_epsilon=1e-5)
def build_backbone_3d( input_specs: tf.keras.layers.InputSpec, model_config, l2_regularizer: tf.keras.regularizers.Regularizer = None): """Builds 3d backbone from a config. Args: input_specs: tf.keras.layers.InputSpec. model_config: a OneOfConfig. Model config. l2_regularizer: tf.keras.regularizers.Regularizer instance. Default to None. Returns: tf.keras.Model instance of the backbone. """ backbone_type = model_config.backbone.type backbone_cfg = model_config.backbone.get() norm_activation_config = model_config.norm_activation # Flatten configs before passing to the backbone. temporal_strides = [] temporal_kernel_sizes = [] use_self_gating = [] for block_spec in backbone_cfg.block_specs: temporal_strides.append(block_spec.temporal_strides) temporal_kernel_sizes.append(block_spec.temporal_kernel_sizes) use_self_gating.append(block_spec.use_self_gating) if backbone_type == 'resnet_3d': backbone = backbones.ResNet3D( model_id=backbone_cfg.model_id, temporal_strides=temporal_strides, temporal_kernel_sizes=temporal_kernel_sizes, use_self_gating=use_self_gating, input_specs=input_specs, activation=norm_activation_config.activation, use_sync_bn=norm_activation_config.use_sync_bn, norm_momentum=norm_activation_config.norm_momentum, norm_epsilon=norm_activation_config.norm_epsilon, kernel_regularizer=l2_regularizer) else: raise ValueError('Backbone {!r} not implement'.format(backbone_type)) return backbone
def test_serialize_deserialize(self): """Validate the classification network can be serialized and deserialized.""" model_id = 50 temporal_strides = [1, 1, 1, 1] temporal_kernel_sizes = [(3, 3, 3), (3, 1, 3, 1), (3, 1, 3, 1, 3, 1), (1, 3, 1)] backbone = backbones.ResNet3D( model_id=model_id, temporal_strides=temporal_strides, temporal_kernel_sizes=temporal_kernel_sizes) model = video_classification_model.VideoClassificationModel( backbone=backbone, num_classes=1000) config = model.get_config() new_model = video_classification_model.VideoClassificationModel.from_config( config) # Validate that the config can be forced to JSON. _ = new_model.to_json() # If the serialization was successful, the new config should match the old. self.assertAllEqual(model.get_config(), new_model.get_config())