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119 lines
4.1 KiB
119 lines
4.1 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""
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Upsampling
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"""
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from __future__ import with_statement, print_function, absolute_import
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import numpy as np
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from mindspore import nn
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from mindspore.ops import operations as P
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class Resize(nn.Cell):
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"""
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Resize input Tensor
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"""
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def __init__(self, x_scale, y_scale, mode="nearest"):
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super(Resize, self).__init__()
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self.x_scale = x_scale
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self.y_scale = y_scale
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self.mode = mode
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def construct(self, x):
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_, _, h, w = x.shape
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interpolate_op = P.ResizeNearestNeighbor((self.y_scale * h, self.x_scale * w))
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return interpolate_op(x)
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def _get_activation(upsample_activation):
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"""get activation"""
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nonlinear = getattr(nn, upsample_activation)
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return nonlinear
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class UpsampleNetwork(nn.Cell):
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"""UpsampleNetwork"""
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def __init__(self, upsample_scales, mode="nearest",
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freq_axis_kernel_size=1, cin_pad=0, cin_channels=80):
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super(UpsampleNetwork, self).__init__()
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self.expand_op = P.ExpandDims()
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self.squeeze_op = P.Squeeze(1)
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up_layers = []
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total_scale = np.prod(upsample_scales)
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self.indent = cin_pad * total_scale
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for scale in upsample_scales:
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freq_axis_padding = (freq_axis_kernel_size - 1) // 2
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k_size = (freq_axis_kernel_size, scale * 2 + 1)
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# padding = (freq_axis_padding, scale)
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padding = (freq_axis_padding, freq_axis_padding, scale, scale)
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stretch = Resize(scale, 1, mode)
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conv = nn.Conv2d(1, 1, kernel_size=k_size, has_bias=False, pad_mode='pad', padding=padding)
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up_layers.append(stretch)
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up_layers.append(conv)
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# if upsample_activation != "none":
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# nonlinear = _get_activation(upsample_activation)
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# up_layers.append(nonlinear(**upsample_activation_params))
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self.up_layers = nn.CellList(up_layers)
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def construct(self, c):
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"""
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Args:
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c (Tensor): Local conditioning feature
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Returns:
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Tensor: Upsampling feature
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"""
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# B x 1 x C x T
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c = self.expand_op(c, 1)
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for f in self.up_layers:
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c = f(c)
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# B x C x T
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c = self.squeeze_op(c)
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# if self.indent > 0:
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# c = c[:, :, self.indent:-self.indent]
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return c
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class ConvInUpsampleNetwork(nn.Cell):
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"""Upsample Network
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Args:
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upsample_scales (list): Upsample_scales list.
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upsample_activation (str): Upsample_activation.
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mode (str): Resize mode, default is NearestNeighbor.
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cin_channels (int): Local conditioning channels.
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freq_axis_kernel_size (int): Freq-axis kernel_size for the convolution layers after resize.
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"""
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def __init__(self, upsample_scales, mode="nearest",
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freq_axis_kernel_size=1, cin_pad=0,
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cin_channels=80):
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super(ConvInUpsampleNetwork, self).__init__()
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ks = 2 * cin_pad + 1
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self.conv_in = nn.Conv1d(cin_channels, cin_channels, kernel_size=ks, has_bias=False, pad_mode='pad', padding=0)
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self.upsample = UpsampleNetwork(upsample_scales, mode, freq_axis_kernel_size, cin_pad=0,
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cin_channels=cin_channels)
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def construct(self, c):
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c = self.conv_in(c)
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c_up = self.upsample(c)
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return c_up
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