From dafd68b740914776a7b545f57abcd49bad9cbd19 Mon Sep 17 00:00:00 2001 From: channingss Date: Fri, 9 Aug 2019 12:52:27 +0800 Subject: [PATCH] fix bug in pool op for tiny_volov2 --- x2paddle/decoder/onnx_decoder.py | 4 +-- x2paddle/op_mapper/onnx_directly_map.py | 4 ++- x2paddle/op_mapper/onnx_op_mapper.py | 36 ++++++++++++++++++++----- x2paddle_model_zoo.md | 8 ++++-- 4 files changed, 40 insertions(+), 12 deletions(-) diff --git a/x2paddle/decoder/onnx_decoder.py b/x2paddle/decoder/onnx_decoder.py index 5ecd3c6..ad66a14 100644 --- a/x2paddle/decoder/onnx_decoder.py +++ b/x2paddle/decoder/onnx_decoder.py @@ -181,6 +181,7 @@ class ONNXGraph(Graph): layer, layer_name=layer.name, is_global_input=is_place_holder) + #set data node's weight for name, weight in self.graph_weights(self.model): if name in self.node_map: @@ -405,8 +406,7 @@ class ONNXDecoder(object): for item in graph.value_info: item.name = self.make_variable_name(item.name) for node in graph.node: - if node.name == '': - node.name = node.output[0] + node.name = node.output[0] node.name = self.make_variable_name(node.name) for i in range(len(node.input)): node.input[i] = self.make_variable_name(node.input[i]) diff --git a/x2paddle/op_mapper/onnx_directly_map.py b/x2paddle/op_mapper/onnx_directly_map.py index 75c2a84..c6edbfb 100644 --- a/x2paddle/op_mapper/onnx_directly_map.py +++ b/x2paddle/op_mapper/onnx_directly_map.py @@ -45,7 +45,9 @@ default_op_mapping = { 'reduce_mean', ['X'], ['Out'], dict(axes='dim', keepdims='keep_dim'), dict(keep_dim=1) - ] + ], + 'LeakyRelu': ['leaky_relu', ['X'], ['Out'], + dict(), dict(alpha=.01)] } default_ioa_constraint = { diff --git a/x2paddle/op_mapper/onnx_op_mapper.py b/x2paddle/op_mapper/onnx_op_mapper.py index e15ec21..c1e2132 100644 --- a/x2paddle/op_mapper/onnx_op_mapper.py +++ b/x2paddle/op_mapper/onnx_op_mapper.py @@ -36,6 +36,14 @@ def _const_weight_or_none(node): return None +def get_same_padding(in_size, kernel_size, stride): + new_size = int(math.ceil(in_size * 1.0 / stride)) + pad_size = (new_size - 1) * stride + kernel_size - in_size + pad0 = int(pad_size / 2) + pad1 = pad_size - pad0 + return [pad0, pad1] + + class ONNXOpMapper(OpMapper): def __init__(self, decoder): super(ONNXOpMapper, self).__init__() @@ -397,9 +405,8 @@ class ONNXOpMapper(OpMapper): def AveragePool(self, node): val_x = self.graph.get_node(node.layer.input[0], copy=True) - assert node.get_attr( - 'auto_pad', - 'NOTSET') == 'NOTSET', 'only auto_pad = NOTSET is supported' + + auto_pad = node.get_attr('auto_pad', 'NOTSET') kernel_shape = node.get_attr("kernel_shape") poolnd = len(kernel_shape) strides = node.get_attr("strides") @@ -408,8 +415,16 @@ class ONNXOpMapper(OpMapper): pads = node.get_attr('pads', [0] * (poolnd * 2)) fluid_op = 'pool{}d'.format(poolnd) assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported' - paddings, val_x = self._pad_if_asymmetric(node, pads, val_x) + + input_shape = val_x.out_shapes + if auto_pad == "SAME_UPPER" or auto_pad == "SAME_UPPER": + pad_h = get_same_padding(input_shape[2], kernel_shape[0], + strides[0]) + pad_w = get_same_padding(input_shape[3], kernel_shape[1], + strides[1]) + attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} + attr = { "pool_size": kernel_shape, "pool_type": string('avg'), @@ -621,10 +636,8 @@ class ONNXOpMapper(OpMapper): def MaxPool(self, node): val_x = self.graph.get_node(node.layer.input[0], copy=True) - assert node.get_attr( - 'auto_pad', 'NOTSET' - ) == 'NOTSET', 'only auto_pad = NOTSET is supported' # optional + auto_pad = node.get_attr('auto_pad', 'NOTSET') assert node.get_attr( "dilations") is None, 'only dilations = 0 is supported' # optional @@ -637,6 +650,15 @@ class ONNXOpMapper(OpMapper): fluid_op = 'pool{}d'.format(poolnd) assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported' paddings, val_x = self._pad_if_asymmetric(node, pads, val_x) + + input_shape = val_x.out_shapes + if auto_pad == "SAME_UPPER" or auto_pad == "SAME_UPPER": + pad_h = get_same_padding(input_shape[2], kernel_shape[0], + strides[0]) + pad_w = get_same_padding(input_shape[3], kernel_shape[1], + strides[1]) + attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} + attr = { "pool_size": kernel_shape, "pool_type": string("max"), diff --git a/x2paddle_model_zoo.md b/x2paddle_model_zoo.md index 6aaf749..c051111 100644 --- a/x2paddle_model_zoo.md +++ b/x2paddle_model_zoo.md @@ -42,17 +42,21 @@ | Alexnet | [torchvison.model.alexnet](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| +| Mobilenet_v2 | [pytorch(personal practice)](https://github.com/tonylins/pytorch-mobilenet-v2) |9| -目前onnx2paddle主要支持onnx operator version 9，关于如何使用torchvison的model: +目前onnx2paddle主要支持onnx operator version 9； +如何将torchvison或者个人开发者写的pytroch model转换成onnx model: ``` import torch import torchvision #根据不同模型调整输入的shape dummy_input = torch.randn(1, 3, 224, 224) + +#预训练后的pytorch model resnet18 = torchvision.models.resnet18(pretrained=True) -#"resnet18.onnx"为onnx model的存储路径 +#"resnet18.onnx"为onnx model的存储路径，1.1 torch.onnx.export(resnet18, dummy_input, "resnet18.onnx",verbose=True) ``` -- GitLab