adapt for dev paddle

x2paddle/convert.py

@@ -98,29 +98,12 @@ def tf2paddle(model_path,
 
     print("Now translating model from tensorflow to paddle.")
     model = TFDecoder(model_path, define_input_shape=define_input_shape)
-    if not without_data_format_optimization:
-        mapper = TFOpMapper(model)
-        optimizer = TFOptimizer(mapper)
-        # neccesary optimization
-        optimizer.delete_redundance_code()
-        # optimizer below is experimental
-        optimizer.optimize_elementwise_op()
-        optimizer.merge_activation()
-        optimizer.merge_bias()
-        optimizer.optimize_sub_graph()
-
-
-#        optimizer.merge_batch_norm()
-#        optimizer.merge_prelu()
-    else:
         mapper = TFOpMapperNHWC(model)
         optimizer = TFOptimizer(mapper)
         optimizer.delete_redundance_code()
         optimizer.strip_graph()
-        optimizer.merge_activation()
-        optimizer.merge_bias()
-        optimizer.make_nchw_input_output()
-        optimizer.remove_transpose()
+#        optimizer.merge_activation()
+#        optimizer.merge_bias()
     mapper.save_inference_model(save_dir)
 
 
@@ -189,14 +172,6 @@ def main():
     assert args.framework is not None, "--framework is not defined(support tensorflow/caffe/onnx)"
     assert args.save_dir is not None, "--save_dir is not defined"
 
-    try:
-        import paddle
-        v0, v1, v2 = paddle.__version__.split('.')
-        if int(v0) != 1 or int(v1) < 5:
-            print("paddlepaddle>=1.5.0 is required")
-            return
-    except:
-        print("paddlepaddle not installed, use \"pip install paddlepaddle\"")
 
     if args.framework == "tensorflow":
         assert args.model is not None, "--model should be defined while translating tensorflow model"

x2paddle/core/fluid_code.py

@@ -80,6 +80,11 @@ class Layer(object):
 
         param_attr = collections.OrderedDict(self.param_attr)
         for key, value in param_attr.items():
+            if isinstance(value, GraphNode):
+                value_name = value.layer_name
+                if hasattr(value, "index"):
+                    value_name += "[{}]".format(value.index)
+                value = value_name
             if '\n' in str(value):
                 value = string(str(value).replace('\n', ','))
             layer_code = layer_code + key + "={}, ".format(value)

x2paddle/decoder/tf_decoder.py

@@ -389,26 +389,10 @@ class TFDecoder(object):
         compare01 = (results[0] == results[1])
         compare12 = (results[1] == results[2])
 
-        if compare01.all() and compare12.all():
-            return results[0].tolist()
-
-        if (compare01 == compare12).all():
-            index = numpy.argwhere(compare01 == False).flatten()
-            if index.shape[0] != 1:
-                raise Exception("There's not only one unstable dimension")
-            results[0][index[0]] = -1
-
-            index = numpy.argwhere(results[0] < 0).flatten()
-            if index.shape[0] > 2:
-                print("Warning: More than two dimension less than zero")
-            if index.shape[0] == 2 and out_shape is not None:
-                if out_shape[index[1]] > 0:
-                    results[0][index[1]] = out_shape[index[1]]
-                else:
-                    results[0][index[0]] = out_shape[index[0]]
-            return results[0].tolist()
-        else:
-            raise Exception("Couldn't infer a stable shape shape tensor value")
+        compare = compare01 & compare12
+        index = numpy.argwhere(compare==False).flatten()
+        results[0][index] = -1
+        return results[0].tolist()
 
     def infer_tensor_shape(self, graph_node):
         if hasattr(graph_node, "index"):
@@ -436,11 +420,7 @@ class TFDecoder(object):
         if compare01.all() and compare12.all():
             return shape[0].tolist()
 
-        if (compare01 == compare12).all():
-            index = numpy.argwhere(compare01 == False).flatten()
-            if index.shape[0] != 1:
-                raise Exception("There's not only one unstable dimension")
-            if index[0] != 0:
-                raise Exception("Batch size not in the first dimension")
-            shapes[0][0] = -1
-            return shapes[0].tolist()
+        compare = compare01 & compare12
+        index = numpy.argwhere(compare==False).flatten()
+        shapes[0][index] = -1
+        return shapes[0].tolist()