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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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# code was heavily based on https://github.com/rosinality/stylegan2-pytorch
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# MIT License
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# Copyright (c) 2019 Kim Seonghyeon
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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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class FusedLeakyReLU(nn.Layer):
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def __init__(self, channel, bias=True, negative_slope=0.2, scale=2**0.5):
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super().__init__()
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if bias:
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self.bias = self.create_parameter(
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(channel, ), default_initializer=nn.initializer.Constant(0.0))
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else:
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self.bias = None
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self.negative_slope = negative_slope
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self.scale = scale
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def forward(self, input):
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return fused_leaky_relu(input, self.bias, self.negative_slope,
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self.scale)
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def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2**0.5):
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if bias is not None:
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rest_dim = [1] * (len(input.shape) - len(bias.shape) - 1)
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return (F.leaky_relu(
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input + bias.reshape((1, bias.shape[0], *rest_dim)),
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negative_slope=0.2) * scale)
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else:
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return F.leaky_relu(input, negative_slope=0.2) * scale
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