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# 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 math
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class _SpectralNorm(nn.SpectralNorm):
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def __init__(self,
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weight_shape,
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dim=0,
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power_iters=1,
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eps=1e-12,
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dtype='float32'):
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super(_SpectralNorm, self).__init__(weight_shape, dim, power_iters, eps,
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dtype)
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def forward(self, weight):
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inputs = {'Weight': weight, 'U': self.weight_u, 'V': self.weight_v}
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out = self._helper.create_variable_for_type_inference(self._dtype)
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_power_iters = self._power_iters if self.training else 0
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self._helper.append_op(
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type="spectral_norm",
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inputs=inputs,
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outputs={"Out": out, },
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attrs={
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"dim": self._dim,
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"power_iters": _power_iters,
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"eps": self._eps,
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})
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return out
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class Spectralnorm(paddle.nn.Layer):
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def __init__(self, layer, dim=0, power_iters=1, eps=1e-12, dtype='float32'):
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super(Spectralnorm, self).__init__()
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self.spectral_norm = _SpectralNorm(layer.weight.shape, dim, power_iters,
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eps, dtype)
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self.dim = dim
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self.power_iters = power_iters
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self.eps = eps
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self.layer = layer
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weight = layer._parameters['weight']
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del layer._parameters['weight']
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self.weight_orig = self.create_parameter(
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weight.shape, dtype=weight.dtype)
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self.weight_orig.set_value(weight)
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def forward(self, x):
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weight = self.spectral_norm(self.weight_orig)
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self.layer.weight = weight
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out = self.layer(x)
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return out
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class RhoClipper(object):
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def __init__(self, min, max):
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self.clip_min = min
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self.clip_max = max
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assert min < max
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def __call__(self, module):
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if hasattr(module, 'rho'):
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w = module.rho
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w = w.clip(self.clip_min, self.clip_max)
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module.rho.set_value(w)
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# used for photo2cartoon training
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if hasattr(module, 'w_gamma'):
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w = module.w_gamma
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w = w.clip(self.clip_min, self.clip_max)
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module.w_gamma.set_value(w)
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if hasattr(module, 'w_beta'):
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w = module.w_beta
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w = w.clip(self.clip_min, self.clip_max)
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module.w_beta.set_value(w)
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