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@@ -327,6 +327,130 @@ class BaseAPI:
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logging.info(
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"Model for inference deploy saved in {}.".format(save_dir))
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+ # def export_onnx_model(self, save_dir, onnx_model=None):
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+ # from fluid.utils import op_io_info, init_name_prefix
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+ # from onnx import helper, checker
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+ # import fluid_onnx.ops as ops
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+ # from fluid_onnx.variables import paddle_variable_to_onnx_tensor, paddle_onnx_weight
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+ # from debug.model_check import debug_model, Tracke
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+ # place = fluid.CPUPlace()
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+ # exe = fluid.Executor(place)
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+ # inference_scope = fluid.core.Scope()
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+ # with fluid.scope_guard(inference_scope):
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+ # test_input_names = [
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+ # var.name for var in list(self.test_inputs.values())
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+ # ]
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+ # inputs_outputs_list = ["fetch", "feed"]
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+ # weights, weights_value_info = [], []
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+ # global_block = self.test_program.global_block()
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+ # for var_name in global_block.vars:
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+ # var = global_block.var(var_name)
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+ # if var_name not in feed_fetch_list\
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+ # and var.persistable:
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+ # weight, val_info = paddle_onnx_weight(
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+ # var=var, scope=inference_scope)
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+ # weights.append(weight)
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+ # weights_value_info.append(val_info)
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+ # # Create inputs
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+ # inputs = [
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+ # paddle_variable_to_onnx_tensor(v, global_block)
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+ # for v in test_input_names
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+ # ]
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+ # print("load the model parameter done.")
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+ # onnx_nodes = []
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+ # op_check_list = []
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+ # op_trackers = []
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+ # nms_first_index = -1
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+ # nms_outputs = []
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+ # for block in inference_program.blocks:
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+ # for op in block.ops:
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+ # if op.type in ops.node_maker:
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+ # # TODO(kuke): deal with the corner case that vars in
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+ # # different blocks have the same name
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+ # node_proto = ops.node_maker[str(op.type)](operator=op,
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+ # block=block)
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+ # op_outputs = []
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+ # last_node = None
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+ # if isinstance(node_proto, tuple):
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+ # onnx_nodes.extend(list(node_proto))
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+ # last_node = list(node_proto)
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+ # else:
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+ # onnx_nodes.append(node_proto)
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+ # last_node = [node_proto]
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+ # tracker = Tracker(str(op.type), last_node)
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+ # op_trackers.append(tracker)
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+ # op_check_list.append(str(op.type))
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+ # if op.type == "multiclass_nms" and nms_first_index < 0:
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+ # nms_first_index = 0
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+ # if nms_first_index >= 0:
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+ # _, _, output_op = op_io_info(op)
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+ # for output in output_op:
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+ # nms_outputs.extend(output_op[output])
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+ # else:
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+ # if op.type not in ['feed', 'fetch']:
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+ # op_check_list.append(op.type)
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+ # print('The operator sets to run test case.')
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+ # print(set(op_check_list))
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+ # # Create outputs
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+ # # Get the new names for outputs if they've been renamed in nodes' making
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+ # renamed_outputs = op_io_info.get_all_renamed_outputs()
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+ # test_outputs = list(self.test_outputs.values())
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+ # test_outputs_names = [var.name for var in self.test_outpus.values]
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+ # test_outputs_names = [
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+ # name if name not in renamed_outputs else renamed_outputs[name]
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+ # for name in test_outputs_names
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+ # ]
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+ # outputs = [
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+ # paddle_variable_to_onnx_tensor(v, global_block)
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+ # for v in test_outputs_names
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+ # ]
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+ # # Make graph
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+ # #model_name = os.path.basename(args.fluid_model.strip('/')).split('.')[0]
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+ # model_name = 'test'
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+ # onnx_graph = helper.make_graph(
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+ # nodes=onnx_nodes,
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+ # name=model_name,
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+ # initializer=weights,
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+ # inputs=inputs + weights_value_info,
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+ # outputs=outputs)
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+
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+ # # Make model
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+ # onnx_model = helper.make_model(onnx_graph, producer_name='PaddlePaddle')
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+
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+ # # Model check
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+ # checker.check_model(onnx_model)
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+
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+ # # Print model
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+ # #if to_print_model:
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+ # # print("The converted model is:\n{}".format(onnx_model))
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+ # # Save converted model
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+ #
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+ # if onnx_model is not None:
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+ # try:
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+ # onnx_model_file = osp.join(save_dir, onnx_model)
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+ # with open(onnx_model_file, 'wb') as f:
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+ # f.write(onnx_model.SerializeToString())
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+ # print("Saved converted model to path: %s" % onnx_model_file)
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+ # # If in debug mode, need to save op list, add we will check op
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+ # #if args.debug:
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+ # # op_check_list = list(set(op_check_list))
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+ # # check_outputs = []
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+
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+ # # for node_proto in onnx_nodes:
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+ # # check_outputs.extend(node_proto.output)
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+
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+ # # print("The num of %d operators need to check, and %d op outputs need to check."\
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+ # # %(len(op_check_list), len(check_outputs)))
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+
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+ # # debug_model(op_check_list, op_trackers, nms_outputs, args)
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+
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+ # except Exception as e:
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+ # print(e)
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+ # print(
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+ # "Convert Failed! Please use the debug message to find error."
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+ # )
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+ # sys.exit(-1)
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+
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def train_loop(self,
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num_epochs,
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train_dataset,
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