Function eval_layer

tensorlayer/files/utils.py:228–254

(layer_kwargs)

def eval_layer(layer_kwargs):
    layer_class = layer_kwargs.pop('class')
    args = layer_kwargs['args']
    layer_type = args.pop('layer_type')
    if layer_type == "normal":
        generate_func(args)
        return eval('tl.layers.' + layer_class)(**args)
    elif layer_type == "layerlist":
        ret_layer = []
        layers = args["layers"]
        for layer_graph in layers:
            ret_layer.append(eval_layer(layer_graph))
        args['layers'] = ret_layer
        return eval('tl.layers.' + layer_class)(**args)
    elif layer_type == "modellayer":
        M = static_graph2net(args['model'])
        args['model'] = M
        return eval('tl.layers.' + layer_class)(**args)
    elif layer_type == "keraslayer":
        M = load_keras_model(args['fn'])
        input_shape = args.pop('keras_input_shape')
        _ = M(np.random.random(input_shape).astype(np.float32))
        args['fn'] = M
        args['fn_weights'] = M.trainable_variables
        return eval('tl.layers.' + layer_class)(**args)
    else:
        raise RuntimeError("Unknown layer type.")


def static_graph2net(model_config):