fix the lrn

x2paddle/op_mapper/dygraph/onnx2paddle/onnx_custom_layer/__init__.py

@@ -18,3 +18,4 @@ 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

x2paddle/op_mapper/dygraph/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
\ No newline at end of file

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

@@ -542,7 +542,6 @@ class OpSet9():
                         value=value,
                         mode=string(mode))
             else:
-                print(pads_len)
                 raise Exception("The padding value is wrong!")   
             if not op_independent:
                 return node.name + '_paded'
@@ -2031,7 +2030,7 @@ class OpSet9():
             'k': bias
         }
         self.paddle_graph.add_layer(
-            "paddle.nn.LocalResponseNorm", 
+            "custom_layer:LocalResponseNorm", 
             inputs={"x": val_x.name}, 
             outputs=layer_outputs, 
             **layer_attrs)

x2paddle/op_mapper/static/onnx2paddle/onnx_custom_layer/__init__.py

@@ -18,3 +18,4 @@ from .pad_two_input import pad_with_two_input
 from .pad_all_dim2 import pad_all_dim2
 from .pad_all_dim4 import pad_all_dim4
 from .pad_all_dim4_one_input import pad_all_dim4_one_input
+from .lrn import local_response_norm
\ No newline at end of file

x2paddle/op_mapper/static/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
+
+def local_response_norm(x, size, alpha=1e-4, beta=0.75, k=1.):
+    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 = [size // 2, (size - 1) // 2, 0, 0]
+    pool2d_shape = (1, size)
+    pad5d_shape = [size // 2, (size - 1) // 2, 0, 0, 0, 0]
+    pool3d_shape = (1, 1, 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=alpha, bias=k)
+    div = paddle.pow(div, beta)
+    res = paddle.divide(x, div)
+    return res
\ No newline at end of file

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

@@ -1797,7 +1797,7 @@ class OpSet9():
             'k': bias
         }
         self.paddle_graph.add_layer(
-            'paddle.nn.functional.local_response_norm', 
+            "custom_layer:local_response_norm", 
             inputs={"x": val_x.name}, 
             outputs=[node.name], 
             **layer_attrs)