fixed LRN bug (#631)

x2paddle/op_mapper/onnx2paddle/onnx_custom_layer/__init__.py

@@ -17,5 +17,4 @@ from .pad_two_input import PadWithTwoInput
 from .pad_all_dim2 import PadAllDim2
 from .pad_all_dim4 import PadAllDim4
 from .pad_all_dim4_one_input import PadAllDim4WithOneInput
-from .lrn import LocalResponseNorm
 from .nms import NMS

x2paddle/op_mapper/onnx2paddle/onnx_custom_layer/lrn.py

-# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import paddle
-
-
-class LocalResponseNorm(object):
-    def __init__(self, size, alpha=1e-4, beta=0.75, k=1.):
-        self.size = size
-        self.alpha = alpha
-        self.beta = beta
-        self.k = k
-
-    def __call__(self, x):
-        sizes = x.shape
-        dim = len(sizes)
-        if dim < 3:
-            raise ValueError(
-                'Expected 3D or higher dimensionality input, but got {} dimensions'.
-                format(dim))
-        div = paddle.unsqueeze(paddle.multiply(x, x), axis=1)
-        pad4d_shape = [self.size // 2, (self.size - 1) // 2, 0, 0]
-        pool2d_shape = (1, self.size)
-        pad5d_shape = [self.size // 2, (self.size - 1) // 2, 0, 0, 0, 0]
-        pool3d_shape = (1, 1, self.size)
-
-        if dim == 3:
-            div = paddle.nn.functional.pad(div, pad=pad4d_shape)
-            div = paddle.nn.functional.avg_pool2d(
-                div, kernel_size=pool2d_shape, stride=1)
-            div = paddle.squeeze(div, axis=1)
-        else:
-            tmp = paddle.unsqueeze(x, axis=1)
-            reshape_shape = paddle.shape(tmp)
-            new_reshape_shape = paddle.cast(reshape_shape, "float32")
-            index = paddle.full(shape=[1], fill_value=-2, dtype="int32")
-            value = paddle.full(shape=[1], fill_value=-1, dtype="float32")
-            new_reshape_shape = paddle.scatter(new_reshape_shape, index, value)
-            new_reshape_shape = paddle.cast(new_reshape_shape, "int32")
-            div = paddle.reshape(div, shape=reshape_shape)
-            div = paddle.nn.functional.pad(div,
-                                           pad=pad5d_shape,
-                                           data_format='NCDHW')
-            div = paddle.nn.functional.avg_pool3d(
-                div, kernel_size=pool3d_shape, stride=1)
-            div = paddle.reshape(paddle.squeeze(div, axis=1), sizes)
-
-        div = paddle.scale(div, scale=self.alpha, bias=self.k)
-        div = paddle.pow(div, self.beta)
-        res = paddle.divide(x, div)
-        return res

x2paddle/op_mapper/onnx2paddle/opset9/opset.py

@@ -2148,7 +2148,7 @@ class OpSet9():
         size = node.get_attr('size')
         layer_attrs = {'size': size, 'alpha': alpha, 'beta': beta, 'k': bias}
         self.paddle_graph.add_layer(
-            "custom_layer:LocalResponseNorm",
+            "paddle.nn.LocalResponseNorm",
             inputs={"x": val_x.name},
             outputs=layer_outputs,
             **layer_attrs)