add weight_norm & remove_weight_norm (#26131)
* add weight_norm, test=developrevert-24895-update_cub
ceci3
5 years ago
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fd66d76231
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy
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import collections
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from functools import reduce
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.nn.utils import weight_norm, remove_weight_norm
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class TestDygraphWeightNorm(unittest.TestCase):
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def setUp(self):
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self.init_test_case()
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self.set_data()
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def init_test_case(self):
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self.batch_size = 3
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self.data_desc = (['x', [2, 3, 3]], )
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self.dim = None
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def set_data(self):
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self.data = collections.OrderedDict()
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for desc in self.data_desc:
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data_name = desc[0]
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data_shape = desc[1]
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data_value = numpy.random.random(
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size=[self.batch_size] + data_shape).astype('float32')
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self.data[data_name] = data_value
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def norm_except_dim(self, w, dim=None):
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shape = w.shape
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ndims = len(shape)
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shape_numel = reduce(lambda x, y: x * y, shape)
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if dim == -1:
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return numpy.linalg.norm(w, axis=None, keepdims=True)
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elif dim == 0:
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tile_shape = list(w.shape)
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tile_shape[0] = 1
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w_matrix = numpy.reshape(w, (shape[0], shape_numel // shape[0]))
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return numpy.linalg.norm(w_matrix, axis=1, keepdims=True)
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elif dim == (ndims - 1):
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w_matrix = numpy.reshape(w, (shape_numel // shape[-1], shape[-1]))
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return numpy.linalg.norm(w_matrix, axis=0, keepdims=True)
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else:
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perm = list(range(ndims))
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perm_ori = list(range(ndims))
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perm[0] = dim
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perm[dim] = 0
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p_transposed = numpy.transpose(w, perm)
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return self.norm_except_dim(p_transposed, 0)
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def weight_normalize(self, w, dim=None):
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shape = w.shape
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ndims = len(shape)
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shape_numel = reduce(lambda x, y: x * y, shape)
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v = w
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g = self.norm_except_dim(w, dim)
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g_mul = g
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if dim == -1:
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v_norm = v / (numpy.linalg.norm(v, axis=None, keepdims=True))
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elif dim == 0:
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w_matrix = numpy.reshape(w, (shape[0], shape_numel // shape[0]))
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v_norm = v / numpy.linalg.norm(w_matrix, axis=1)
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v_norm = numpy.reshape(v_norm, shape)
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g = numpy.squeeze(g, axis=1)
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elif dim == (ndims - 1):
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w_matrix = numpy.reshape(w, (shape_numel // shape[-1], shape[-1]))
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v_norm = v / numpy.linalg.norm(w_matrix, axis=0, keepdims=True)
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v_norm = numpy.reshape(v_norm, shape)
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else:
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perm = list(range(ndims))
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perm[0] = dim
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perm[dim] = 0
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p_transposed = numpy.transpose(v, perm)
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transposed_shape = p_transposed.shape
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transposed_shape_numel = reduce(lambda x, y: x * y,
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transposed_shape)
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p_matrix = numpy.reshape(
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p_transposed, (p_transposed.shape[0],
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transposed_shape_numel // p_transposed.shape[0]))
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v_norm = v / numpy.expand_dims(
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numpy.expand_dims(
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numpy.linalg.norm(
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p_matrix, axis=1, keepdims=True), axis=0),
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axis=(ndims - 1))
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v_norm = numpy.reshape(v_norm, transposed_shape)
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v_norm = numpy.transpose(v_norm, perm)
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g = numpy.squeeze(g, axis=1)
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if dim == 1:
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eaxis = 2
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elif dim == 2:
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eaxis = 1
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g_mul = numpy.expand_dims(
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numpy.expand_dims(
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numpy.expand_dims(
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g, axis=0), axis=eaxis),
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axis=(ndims - 1))
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w = g_mul * v_norm
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return g, v
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def test_check_output(self):
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fluid.enable_imperative()
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linear = paddle.nn.Conv2D(2, 3, 3)
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before_weight = linear.weight.numpy()
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if self.dim == None:
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self.dim = -1
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wn = weight_norm(linear, dim=self.dim)
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outputs = []
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for name, data in self.data.items():
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output = linear(fluid.dygraph.to_variable(data))
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outputs.append(output.numpy())
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after_weight = linear.weight
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self.actual_outputs = [linear.weight_g.numpy(), linear.weight_v.numpy()]
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expect_output = self.weight_normalize(before_weight, self.dim)
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for expect, actual in zip(expect_output, self.actual_outputs):
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self.assertTrue(
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numpy.allclose(
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numpy.array(actual), expect, atol=0.001))
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class TestDygraphWeightNormCase1(TestDygraphWeightNorm):
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def init_test_case(self):
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self.batch_size = 3
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self.data_desc = (['x', [2, 3, 3]], )
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self.dim = 0
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class TestDygraphWeightNormCase2(TestDygraphWeightNorm):
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def init_test_case(self):
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self.batch_size = 3
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self.data_desc = (['x', [2, 3, 3]], )
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self.dim = 1
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class TestDygraphWeightNormCase3(TestDygraphWeightNorm):
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def init_test_case(self):
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self.batch_size = 3
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self.data_desc = (['x', [2, 3, 3]], )
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self.dim = 3
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class TestDygraphRemoveWeightNorm(unittest.TestCase):
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def setUp(self):
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self.init_test_case()
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def init_test_case(self):
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self.batch_size = 3
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self.data_desc = (['x', [2, 3, 3]], )
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self.dim = None
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def test_check_output(self):
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fluid.enable_imperative()
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linear = paddle.nn.Conv2D(2, 3, 3)
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before_weight = linear.weight
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wn = weight_norm(linear, dim=self.dim)
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rwn = remove_weight_norm(linear)
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after_weight = linear.weight
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self.assertTrue(
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numpy.allclose(
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before_weight.numpy(), after_weight.numpy(), atol=0.001))
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if __name__ == '__main__':
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unittest.main()
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from . import weight_norm_hook
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from .weight_norm_hook import weight_norm, remove_weight_norm
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from ... import fluid
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from ...fluid import dygraph
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from ...fluid import layers as F
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from ...fluid.layer_helper import LayerHelper
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from ...fluid.data_feeder import check_variable_and_dtype
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from ...tensor.math import multiply
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__all__ = ['weight_norm', 'remove_weight_norm']
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def l2_norm(x, axis, epsilon=1e-12, name=None):
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if len(x.shape) == 1:
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axis = 0
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check_variable_and_dtype(x, "X", ("float32", "float64"), "norm")
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helper = LayerHelper("l2_normalize", **locals())
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out = helper.create_variable_for_type_inference(dtype=x.dtype)
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norm = helper.create_variable_for_type_inference(dtype=x.dtype)
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helper.append_op(
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type="norm",
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inputs={"X": x},
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outputs={"Out": out,
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"Norm": norm},
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attrs={
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"axis": 1 if axis is None else axis,
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"epsilon": epsilon,
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})
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return F.squeeze(norm, axes=[axis])
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def norm_except_dim(p, dim):
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shape = p.shape
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ndims = len(shape)
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if dim == -1:
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return F.sqrt(F.reduce_sum(F.square(p)) + 1e-12)
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elif dim == 0:
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p_matrix = F.reshape(p, (shape[0], -1))
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return l2_norm(p_matrix, axis=1)
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elif dim == ndims - 1:
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p_matrix = F.reshape(p, (-1, shape[-1]))
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return l2_norm(p_matrix, axis=0)
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else:
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perm = list(range(ndims))
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perm[0] = dim
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perm[dim] = 0
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p_transposed = F.transpose(p, perm)
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return norm_except_dim(p_transposed, 0)
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def _weight_norm(v, g, dim):
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shape = v.shape
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ndims = len(shape)
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if dim == -1:
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v_normalized = v / (F.sqrt(F.reduce_sum(F.square(v))) + 1e-12)
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elif dim == 0:
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p_matrix = F.reshape(v, (shape[0], -1))
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v_normalized = F.l2_normalize(p_matrix, axis=1)
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v_normalized = F.reshape(v_normalized, shape)
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elif dim == ndims - 1:
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p_matrix = F.reshape(v, (-1, shape[-1]))
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v_normalized = F.l2_normalize(p_matrix, axis=0)
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v_normalized = F.reshape(v_normalized, shape)
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else:
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perm = list(range(ndims))
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perm[0] = dim
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perm[dim] = 0
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p_transposed = F.transpose(v, perm)
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transposed_shape = p_transposed.shape
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p_matrix = F.reshape(p_transposed, (p_transposed.shape[0], -1))
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v_normalized = F.l2_normalize(p_matrix, axis=1)
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v_normalized = F.reshape(v_normalized, transposed_shape)
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v_normalized = F.transpose(v_normalized, perm)
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weight = multiply(v_normalized, g, axis=dim if dim is not None else -1)
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return weight
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class WeightNorm(object):
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def __init__(self, name, dim):
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if dim is None:
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dim = -1
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self.name = name
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self.dim = dim
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def compute_weight(self, layer):
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g = getattr(layer, self.name + '_g')
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v = getattr(layer, self.name + '_v')
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return _weight_norm(v, g, self.dim)
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@staticmethod
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def apply(layer, name, dim):
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for k, hook in layer._forward_pre_hooks.items():
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if isinstance(hook, WeightNorm) and hook.name == name:
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raise RuntimeError("Cannot register two weight_norm hooks on "
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"the same parameter {}".format(name))
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if dim is None:
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dim = -1
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fn = WeightNorm(name, dim)
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w = getattr(layer, name)
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del layer._parameters[name]
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g_var = norm_except_dim(w, dim)
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v = layer.create_parameter(w.shape, dtype=w.dtype)
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layer.add_parameter(name + "_v", v)
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g = layer.create_parameter(g_var.shape, dtype=g_var.dtype)
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layer.add_parameter(name + '_g', g)
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with dygraph.no_grad():
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F.assign(w, v)
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F.assign(g_var, g)
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setattr(layer, name, fn.compute_weight(layer))
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layer.register_forward_pre_hook(fn)
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return fn
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def remove(self, layer):
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w_var = self.compute_weight(layer)
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delattr(layer, self.name)
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del layer._parameters[self.name + '_g']
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del layer._parameters[self.name + '_v']
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w = layer.create_parameter(w_var.shape, dtype=w_var.dtype)
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layer.add_parameter(self.name, w)
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with dygraph.no_grad():
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F.assign(w_var, w)
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def __call__(self, layer, inputs):
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setattr(layer, self.name, self.compute_weight(layer))
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def weight_norm(layer, name='weight', dim=0):
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"""
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This weight_norm layer applies weight normalization to a parameter according to the
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following formula:
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.. math::
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\mathbf{w} = g \dfrac{v}{\|v\|}
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Weight normalization is a reparameterization of the weight vectors in a neural network that
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decouples the magnitude of those weight vectors from their direction. Weight normalization
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replaces the parameter specified by `name`(eg: 'weight') with two parameters: one parameter
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specifying the magnitude (eg: 'weight_g') and one parameter specifying the direction
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(eg: 'weight_v'). Weight normalization has been implemented as discussed in this paper:
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`Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
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<https://arxiv.org/pdf/1602.07868.pdf>`_.
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Parameters:
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layer(Layer): Layer of paddle, which has weight.
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name(str, optional): Name of the weight parameter. Default: 'weight'.
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dim(int, optional): Dimension over which to compute the norm. Dim is a non-negative number
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which is less than the rank of weight Tensor. For Example, dim can be chosen from 0,
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1, 2, 3 for convolution whose weight shape is [cout, cin, kh, kw] and rank is 4.
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If dim is set to None, meaning that all elements will be normalized. Default: 0.
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Returns:
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Origin layer with weight norm hook.
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Examples:
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.. code-block:: python
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import numpy as np
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from paddle.nn import Conv2D
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from paddle.nn.utils import weight_norm
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x = np.array([[[[0.3, 0.4], [0.3, 0.07]], [[0.83, 0.37], [0.18, 0.93]]]]).astype('float32')
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paddle.disable_static()
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conv = Conv2D(3, 5, 3)
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wn = weight_norm(conv)
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print(conv.weight_g.shape)
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# [5]
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print(conv.weight_v.shape)
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# [5, 3, 3, 3]
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"""
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WeightNorm.apply(layer, name, dim)
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return layer
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def remove_weight_norm(layer, name='weight'):
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"""
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remove weight normalization from layer.
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Parameters:
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layer(Layer): Layer of paddle, which has weight.
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name(str, optional): Name of the weight parameter. Default: 'weight'.
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Returns:
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Origin layer without weight norm
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Examples:
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.. code-block:: python
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import paddle
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from paddle.nn import Conv2D
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from paddle.nn.utils import weight_norm, remove_weight_norm
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paddle.disable_static()
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conv = Conv2D(3, 5, 3)
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wn = weight_norm(conv)
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remove_weight_norm(conv)
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print(conv.weight_g)
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# AttributeError: 'Conv2D' object has no attribute 'weight_g'
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"""
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for k, hook in layer._forward_pre_hooks.items():
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if isinstance(hook, WeightNorm) and hook.name == name:
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hook.remove(layer)
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del layer._forward_pre_hooks[k]
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return layer
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raise ValueError("weight_norm of '{}' not found in {}".format(name, layer))
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