151 lines
5.4 KiB
Python
151 lines
5.4 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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 paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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def normal_(x, mean=0., std=1.):
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temp_value = paddle.normal(mean, std, shape=x.shape)
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x.set_value(temp_value)
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return x
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class SpectralNorm(object):
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def __init__(self, name='weight', n_power_iterations=1, dim=0, eps=1e-12):
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self.name = name
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self.dim = dim
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if n_power_iterations <= 0:
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raise ValueError('Expected n_power_iterations to be positive, but '
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'got n_power_iterations={}'.format(
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n_power_iterations))
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self.n_power_iterations = n_power_iterations
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self.eps = eps
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def reshape_weight_to_matrix(self, weight):
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weight_mat = weight
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if self.dim != 0:
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# transpose dim to front
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weight_mat = weight_mat.transpose([
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self.dim,
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* [d for d in range(weight_mat.dim()) if d != self.dim]
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])
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height = weight_mat.shape[0]
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return weight_mat.reshape([height, -1])
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def compute_weight(self, module, do_power_iteration):
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weight = getattr(module, self.name + '_orig')
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u = getattr(module, self.name + '_u')
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v = getattr(module, self.name + '_v')
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weight_mat = self.reshape_weight_to_matrix(weight)
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if do_power_iteration:
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with paddle.no_grad():
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for _ in range(self.n_power_iterations):
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v.set_value(
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F.normalize(
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paddle.matmul(
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weight_mat,
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u,
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transpose_x=True,
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transpose_y=False),
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axis=0,
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epsilon=self.eps, ))
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u.set_value(
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F.normalize(
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paddle.matmul(weight_mat, v),
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axis=0,
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epsilon=self.eps, ))
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if self.n_power_iterations > 0:
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u = u.clone()
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v = v.clone()
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sigma = paddle.dot(u, paddle.mv(weight_mat, v))
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weight = weight / sigma
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return weight
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def remove(self, module):
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with paddle.no_grad():
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weight = self.compute_weight(module, do_power_iteration=False)
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delattr(module, self.name)
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delattr(module, self.name + '_u')
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delattr(module, self.name + '_v')
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delattr(module, self.name + '_orig')
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module.add_parameter(self.name, weight.detach())
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def __call__(self, module, inputs):
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setattr(
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module,
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self.name,
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self.compute_weight(
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module, do_power_iteration=module.training))
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@staticmethod
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def apply(module, name, n_power_iterations, dim, eps):
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for k, hook in module._forward_pre_hooks.items():
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if isinstance(hook, SpectralNorm) and hook.name == name:
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raise RuntimeError(
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"Cannot register two spectral_norm hooks on "
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"the same parameter {}".format(name))
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fn = SpectralNorm(name, n_power_iterations, dim, eps)
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weight = module._parameters[name]
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with paddle.no_grad():
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weight_mat = fn.reshape_weight_to_matrix(weight)
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h, w = weight_mat.shape
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# randomly initialize u and v
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u = module.create_parameter([h])
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u = normal_(u, 0., 1.)
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v = module.create_parameter([w])
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v = normal_(v, 0., 1.)
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u = F.normalize(u, axis=0, epsilon=fn.eps)
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v = F.normalize(v, axis=0, epsilon=fn.eps)
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# delete fn.name form parameters, otherwise you can not set attribute
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del module._parameters[fn.name]
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module.add_parameter(fn.name + "_orig", weight)
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# still need to assign weight back as fn.name because all sorts of
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# things may assume that it exists, e.g., when initializing weights.
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# However, we can't directly assign as it could be an Parameter and
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# gets added as a parameter. Instead, we register weight * 1.0 as a plain
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# attribute.
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setattr(module, fn.name, weight * 1.0)
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module.register_buffer(fn.name + "_u", u)
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module.register_buffer(fn.name + "_v", v)
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module.register_forward_pre_hook(fn)
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return fn
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def spectral_norm(module,
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name='weight',
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n_power_iterations=1,
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eps=1e-12,
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dim=None):
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if dim is None:
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if isinstance(module, (nn.Conv1DTranspose, nn.Conv2DTranspose,
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nn.Conv3DTranspose, nn.Linear)):
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dim = 1
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else:
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dim = 0
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SpectralNorm.apply(module, name, n_power_iterations, dim, eps)
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return module
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