update ONNX supported models info

test_model_zoo.md

@@ -26,3 +26,30 @@
 | ShuffleNet | [code](https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe/releases/tag/v0.1.0) |
 | mNASNet | [code](https://github.com/LiJianfei06/MnasNet-caffe) |
 | MTCNN | [code](https://github.com/kpzhang93/MTCNN_face_detection_alignment/tree/master/code/codes/MTCNNv1/model) |
+
+# ONNX
+
+| 模型 | 来源 | operator version|
+|-------|--------|
+| Resnet18 | [torchvison.model.resnet18](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py) |9|
+| Resnet34 | [torchvison.model.resnet34](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py) |9|
+| Resnet50 | [torchvison.model.resnet50](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py) |9|
+| Resnet101 | [torchvison.model.resnet101](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py) |9|
+| Vgg11 | [torchvison.model.vgg11](https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py) |9|
+| Vgg11_bn | [torchvison.model.vgg11_bn](https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py) |9|
+| Vgg19| [torchvison.model.vgg16_bn](https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py) |9|
+| Densenet121 | [torchvison.model.densenet121](https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py) |9|
+| Alexnet | [onnx official](https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py) |9|
+| Shufflenet | [onnx official](https://github.com/onnx/models/tree/master/vision/classification/shufflenet) |9|
+| Inception_v2 | [onnx official](https://github.com/onnx/models/tree/master/vision/classification/inception_and_googlenet/inception_v2) |9|
+
+
+目前onnx2paddle主要支持onnx operator version 9，关于如何使用torchvison的model:
+```
+import torch
+import torchvision
+dummy_input = torch.randn(1, 3, 224, 224) #根据不同模型调整shape
+resnet18 = torchvision.models.resnet18(pretrained=True)
+torch.onnx.export(resnet18, dummy_input, "resnet18.onnx",verbose=True)#"resnet18.onnx"为onnx model的存储路径
+
+```

x2paddle/decoder/onnx_decoder.py

@@ -27,8 +27,10 @@ from collections import OrderedDict as Dict
 import onnx
 import numpy as np
 from copy import deepcopy
+import logging as _logging
 
 default_op_domain = 'ai.onnx'
+_logger = _logging.getLogger(__name__)
 
 
 class ONNXGraphNode(GraphNode):
@@ -229,10 +231,17 @@ class ONNXDecoder(object):
     def __init__(self, onnx_model):
         model = onnx.load(onnx_model)
         print('model ir_version: {}, op version: {}'.format(
-            model.ir_version, model.opset_import))
-
+            model.ir_version, model.opset_import[0].version))
+
+        if model.opset_import[0].version < 9:
+            _logger.warning(
+                'Now, onnx2paddle main support convert onnx model opset_verison == 9,'
+                'opset_verison of your onnx model is %d < 9,'
+                'some operator may cannot convert.',
+                model.opset_import[0].version)
         check_model(model)
-        model = convert_version(model, 9)
+
+        #         model = convert_version(model, 10)
         model = polish_model(model)
 
         model = self.optimize_model_skip_op_for_inference(model)
@@ -263,7 +272,6 @@ class ONNXDecoder(object):
         """
         skip nodes between src_output_name -> dst_input_name and connect this pair
         """
-
         processed = 0
         for next_idx in input_refs[src_output_name]:
             next_node = nodes[next_idx]
@@ -278,7 +286,6 @@ class ONNXDecoder(object):
         """
         skip nodes between dst_output_name -> src_input_name and connect this pair
         """
-
         processed = 0
         for prev_idx in output_refs[src_input_name]:
             prev_node = nodes[prev_idx]

x2paddle/op_mapper/onnx_op_mapper.py

@@ -355,16 +355,16 @@ class ONNXOpMapper(OpMapper):
         if shape_dtype is None:
             _logger.warning(
                 'in op %s(%s -> Reshape -> %s): '
-                'dtype of input "shape" not inferred, int32 assumed', name,
-                inputs, outputs)
+                'dtype of input "shape" not inferred, int32 assumed',
+                node.layer_name, val_x.layer_name, val_reshaped.layer_name)
             shape_dtype = _np.dtype('int32')
         if shape is None:
             shape = [1, -1]
             _logger.warning(
                 'in %s(%s -> Reshape -> %s): '
                 'input "shape" not inferred, use [1, -1] as dummy value, '
-                'the behavior of Paddle fluid maybe undefined', name, inputs,
-                outputs)
+                'the behavior of Paddle fluid maybe undefined', node.layer_name,
+                val_x.layer_name, val_reshaped.layer_name)
         attr = {'shape': shape, 'name': string(node.layer_name)}
 
         node.fluid_code.add_layer('reshape',
@@ -532,6 +532,8 @@ class ONNXOpMapper(OpMapper):
         momentum = node.get_attr('momentum', .9)
         epsilon = node.get_attr('epsilon', 1e-5)
 
+        # Attribute: spatial is used in BatchNormalization-1,6,7
+        spatial = bool(node.get_attr('spatial'))
         attr = {
             "momentum": momentum,
             "epsilon": epsilon,
@@ -541,6 +543,7 @@ class ONNXOpMapper(OpMapper):
             "bias_attr": string(val_b.layer_name),
             "moving_mean_name": string(val_mean.layer_name),
             "moving_variance_name": string(val_var.layer_name),
+            "use_global_stats": spatial,
             "name": string(node.layer_name)
         }
         node.fluid_code.add_layer("batch_norm",