Function static_graph2net

tensorlayer/files/utils.py:257–296

(model_config)

def static_graph2net(model_config):
    layer_dict = {}
    model_name = model_config["name"]
    inputs_tensors = model_config["inputs"]
    outputs_tensors = model_config["outputs"]
    all_args = model_config["model_architecture"]
    for idx, layer_kwargs in enumerate(all_args):
        layer_class = layer_kwargs["class"]  # class of current layer
        prev_layers = layer_kwargs.pop("prev_layer")  # name of previous layers
        net = eval_layer(layer_kwargs)
        if layer_class in tl.layers.inputs.__all__:
            net = net._nodes[0].out_tensors[0]
        if prev_layers is not None:
            for prev_layer in prev_layers:
                if not isinstance(prev_layer, list):
                    output = net(layer_dict[prev_layer])
                    layer_dict[output._info[0].name] = output
                else:
                    list_layers = [layer_dict[layer] for layer in prev_layer]
                    output = net(list_layers)
                    layer_dict[output._info[0].name] = output
        else:
            layer_dict[net._info[0].name] = net

    if not isinstance(inputs_tensors, list):
        model_inputs = layer_dict[inputs_tensors]
    else:
        model_inputs = []
        for inputs_tensor in inputs_tensors:
            model_inputs.append(layer_dict[inputs_tensor])
    if not isinstance(outputs_tensors, list):
        model_outputs = layer_dict[outputs_tensors]
    else:
        model_outputs = []
        for outputs_tensor in outputs_tensors:
            model_outputs.append(layer_dict[outputs_tensor])
    from tensorlayer.models import Model
    M = Model(inputs=model_inputs, outputs=model_outputs, name=model_name)
    logging.info("[*] Load graph finished")
    return M


def load_hdf5_graph(filepath='model.hdf5', load_weights=False):