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91 lines
3.0 KiB
91 lines
3.0 KiB
# 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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