optimize code

x2paddle/core/program.py

@@ -521,7 +521,7 @@ class PaddleGraph(object):
                 gen_codes(
                     comment_list,
                     indent=1))
-            use_structured_name = False if self.source_type in ["tf", "onnx"] else True
+            use_structured_name = False if self.source_type in ["tf"] else True
             self.run_func.extend(
                 gen_codes(["paddle.disable_static()",
                            "params = paddle.load('{}/model.pdparams')".format(osp.abspath(code_dir)),
@@ -673,7 +673,7 @@ class PaddleGraph(object):
         paddle.disable_static()
         restore = paddle.load(osp.join(save_dir, "model.pdparams"))
         model = getattr(x2paddle_code, self.name)()
-        if self.source_type in ["tf", "onnx"]:
+        if self.source_type in ["tf"]:
             model.set_dict(restore, use_structured_name=False)
         else:
             model.set_dict(restore)

x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py

@@ -1898,7 +1898,7 @@ class OpSet9():
 
         reform_permutation = [(0, 1), (2, 4), (1, 2)]
 
-        input_weight_np, hidden_weight_np, input_bias_np, hidden_bias_np = transform_weight_with_bias(
+        weights = transform_weight_with_bias(
             [input_weight_np, hidden_weight_np, input_bias_np, hidden_bias_np],
             hidden_size, reform_permutation)
 
@@ -1907,30 +1907,27 @@ class OpSet9():
         yh_out = node.output(1) 
         yc_out = node.output(2)
         direction = node.get_attr('direction', 'forward')
+
+        def generate_paddle_param_names(op_name, suffix=''):
+            param_names = []
+            param_names.extend(['{}.weight_ih_l0{}', '{}.weight_hh_l0{}'])
+            if have_bias != False: param_names.append('{}.bias_ih_l0{}')
+            if have_bias != False: param_names.append('{}.bias_hh_l0{}')
+            param_names = [x.format(op_name, suffix) for x in param_names]
+            return param_names
+
+        def assign_params(op_name, weights, weight_idx=0, suffix=''):
+            param_names = generate_paddle_param_names(op_name, suffix)
+            print(param_names)
+            for param_name, weight in zip(param_names, weights):
+                self.weights[param_name] = weight[weight_idx]
+
         if direction == 'backward':
             raise Exception("LSTM support 'forward' or 'bidirectional', except '{}'.".format(direction))
-        elif direction == 'forward':
-            self.weights[input_weight.name] = input_weight_np.squeeze(0) 
-            self.weights[hidden_weight.name] = hidden_weight_np.squeeze(0)
-            self.weights[input_bias_name] = input_bias_np.squeeze(0)
-            self.weights[hidden_bias_name] = hidden_bias_np.squeeze(0)
         else:
-            param_names = []
-            for direct in range(2):
-                suffix = '_reverse' if direct == 1 else ''
-                param_names.extend(['{}.weight_ih_l0{}', '{}.weight_hh_l0{}'])
-                if have_bias != False: param_names.append('{}.bias_ih_l0{}')
-                if have_bias != False: param_names.append('{}.bias_hh_l0{}')
-                param_names = [x.format(op_name, suffix) for x in param_names]
-
-            self.weights[param_names[0]] = input_weight_np[0] 
-            self.weights[param_names[4]] = input_weight_np[1] 
-            self.weights[param_names[1]] = hidden_weight_np[0] 
-            self.weights[param_names[5]] = hidden_weight_np[1] 
-            self.weights[param_names[2]] = input_bias_np[0] 
-            self.weights[param_names[6]] = input_bias_np[1] 
-            self.weights[param_names[3]] = hidden_bias_np[0] 
-            self.weights[param_names[7]] = hidden_bias_np[1] 
+            assign_params(op_name, weights)
+            if direction == 'bidirectional':
+                assign_params(op_name, weights, 1, '_reverse')
 
         self.paddle_graph.add_layer(
             'paddle.nn.LSTM',