| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| """ |
| Description : LipNet module using gluon |
| """ |
| |
| from mxnet.gluon import nn, rnn |
| # pylint: disable=too-many-instance-attributes |
| class LipNet(nn.HybridBlock): |
| """ |
| Description : LipNet network using gluon |
| dr_rate : Dropout rate |
| """ |
| def __init__(self, dr_rate, **kwargs): |
| super(LipNet, self).__init__(**kwargs) |
| with self.name_scope(): |
| self.conv1 = nn.Conv3D(32, kernel_size=(3, 5, 5), strides=(1, 2, 2), padding=(1, 2, 2)) |
| self.bn1 = nn.InstanceNorm(in_channels=32) |
| self.dr1 = nn.Dropout(dr_rate, axes=(1, 2)) |
| self.pool1 = nn.MaxPool3D((1, 2, 2), (1, 2, 2)) |
| self.conv2 = nn.Conv3D(64, kernel_size=(3, 5, 5), strides=(1, 1, 1), padding=(1, 2, 2)) |
| self.bn2 = nn.InstanceNorm(in_channels=64) |
| self.dr2 = nn.Dropout(dr_rate, axes=(1, 2)) |
| self.pool2 = nn.MaxPool3D((1, 2, 2), (1, 2, 2)) |
| self.conv3 = nn.Conv3D(96, kernel_size=(3, 3, 3), strides=(1, 1, 1), padding=(1, 2, 2)) |
| self.bn3 = nn.InstanceNorm(in_channels=96) |
| self.dr3 = nn.Dropout(dr_rate, axes=(1, 2)) |
| self.pool3 = nn.MaxPool3D((1, 2, 2), (1, 2, 2)) |
| self.gru1 = rnn.GRU(256, bidirectional=True) |
| self.gru2 = rnn.GRU(256, bidirectional=True) |
| self.dense = nn.Dense(27+1, flatten=False) |
| |
| # pylint: disable=arguments-differ |
| def hybrid_forward(self, F, x): |
| out = self.conv1(x) |
| out = self.bn1(out) |
| out = F.relu(out) |
| out = self.dr1(out) |
| out = self.pool1(out) |
| out = self.conv2(out) |
| out = self.bn2(out) |
| out = F.relu(out) |
| out = self.dr2(out) |
| out = self.pool2(out) |
| out = self.conv3(out) |
| out = self.bn3(out) |
| out = F.relu(out) |
| out = self.dr3(out) |
| out = self.pool3(out) |
| out = F.transpose(out, (2, 0, 1, 3, 4)) |
| # pylint: disable=no-member |
| out = out.reshape((0, 0, -1)) |
| out = self.gru1(out) |
| out = self.gru2(out) |
| out = self.dense(out) |
| out = F.log_softmax(out, axis=2) |
| out = F.transpose(out, (1, 0, 2)) |
| return out |
