clmr
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81 lines
2.5 KiB
81 lines
2.5 KiB
# Original implementation by https://github.com/Spijkervet/CLMR
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# Copyright 2021 Zilliz. All rights reserved.
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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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from torch import nn
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from clmr_model import Model
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class SampleCNN(Model):
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def __init__(self, strides, supervised, out_dim):
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super(SampleCNN, self).__init__()
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self.strides = strides
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self.supervised = supervised
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self.sequential = [
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nn.Sequential(
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nn.Conv1d(1, 128, kernel_size=3, stride=3, padding=0),
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nn.BatchNorm1d(128),
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nn.ReLU(),
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)
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]
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self.hidden = [
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[128, 128],
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[128, 128],
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[128, 256],
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[256, 256],
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[256, 256],
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[256, 256],
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[256, 256],
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[256, 256],
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[256, 512],
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]
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assert len(self.hidden) == len(
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self.strides
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), "Number of hidden layers and strides are not equal"
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for stride, (h_in, h_out) in zip(self.strides, self.hidden):
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self.sequential.append(
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nn.Sequential(
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nn.Conv1d(h_in, h_out, kernel_size=stride, stride=1, padding=1),
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nn.BatchNorm1d(h_out),
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nn.ReLU(),
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nn.MaxPool1d(stride, stride=stride),
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)
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)
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# 1 x 512
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self.sequential.append(
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nn.Sequential(
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nn.Conv1d(512, 512, kernel_size=3, stride=1, padding=1),
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nn.BatchNorm1d(512),
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nn.ReLU(),
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)
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)
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self.sequential = nn.Sequential(*self.sequential)
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if self.supervised:
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self.dropout = nn.Dropout(0.5)
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self.fc = nn.Linear(512, out_dim)
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def forward(self, x):
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out = self.sequential(x)
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if self.supervised:
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out = self.dropout(out)
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out = out.reshape(x.shape[0], out.size(1) * out.size(2))
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logit = self.fc(out)
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return logit
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