Python torch.nn.functional.relu() Examples

The following are 30 code examples of torch.nn.functional.relu(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module torch.nn.functional , or try the search function .
Example #1
Source File: model.py    From cat-bbs with MIT License 8 votes vote down vote up 
def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(MyResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        # note the increasing dilation
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilation=1)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4)

        # these layers will not be used
        self.avgpool = nn.AvgPool2d(7)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
Example #2
Source File: model.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.conv1(x))
        out = self.bnm1(out)
        out = F.relu(self.conv2(out))
        out = self.bnm2(out)
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv3(out))
        out = self.bnm3(out)
        out = F.relu(self.conv4(out))
        out = self.bnm4(out)
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        #out = self.dropout1(out)
        out = F.relu(self.fc1(out))
        #out = self.dropout2(out)
        out = self.bnm5(out)
        out = F.relu(self.fc2(out))
        #out = self.dropout3(out)
        out = self.bnm6(out)
        out = self.fc3(out)
        return (out)
Example #3
Source File: model.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.conv1(x))
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv3(out))
        out = F.relu(self.conv4(out))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv5(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = self.dropout1(out)
        out = F.relu(self.fc1(out))
        out = self.dropout2(out)
        out = F.relu(self.fc2(out))
        out = self.dropout3(out)
        out = self.fc3(out)
        return (out)
Example #4
Source File: model.py    From cat-bbs with MIT License 6 votes vote down vote up 
def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out
Example #5
Source File: model.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.conv1(x))
        out = self.bnm1(out)
        out = F.relu(self.conv2(out))
        out = self.bnm2(out)
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv3(out))
        out = self.bnm3(out)
        out = F.relu(self.conv4(out))
        out = self.bnm4(out)
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        #out = self.dropout1(out)
        out = F.relu(self.fc1(out))
        #out = self.dropout2(out)
        out = self.bnm5(out)
        out = F.relu(self.fc2(out))
        #out = self.dropout3(out)
        out = self.bnm6(out)
        out = self.fc3(out)
        return (out)
Example #6
Source File: ssd_vgg.py    From mmdetection with Apache License 2.0 6 votes vote down vote up 
def forward(self, x):
        """Forward function."""
        outs = []
        for i, layer in enumerate(self.features):
            x = layer(x)
            if i in self.out_feature_indices:
                outs.append(x)
        for i, layer in enumerate(self.extra):
            x = F.relu(layer(x), inplace=True)
            if i % 2 == 1:
                outs.append(x)
        outs[0] = self.l2_norm(outs[0])
        if len(outs) == 1:
            return outs[0]
        else:
            return tuple(outs)
Example #7
Source File: densenet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = self.conv(F.relu(self.bn(x)))
        out = F.avg_pool2d(out, 2)
        return out
Example #8
Source File: pnasnet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        # Left branch
        y1 = self.sep_conv1(x)
        y2 = self.sep_conv2(x)
        # Right branch
        y3 = F.max_pool2d(x, kernel_size=3, stride=self.stride, padding=1)
        if self.stride==2:
            y3 = self.bn1(self.conv1(y3))
        y4 = self.sep_conv3(x)
        # Concat & reduce channels
        b1 = F.relu(y1+y2)
        b2 = F.relu(y3+y4)
        y = torch.cat([b1,b2], 1)
        return F.relu(self.bn2(self.conv2(y)))
Example #9
Source File: pnasnet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.layer5(out)
        out = F.avg_pool2d(out, 8)
        out = self.linear(out.view(out.size(0), -1))
        return out
Example #10
Source File: dpn.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        x = self.shortcut(x)
        d = self.out_planes
        out = torch.cat([x[:,:d,:,:]+out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1)
        out = F.relu(out)
        return out
Example #11
Source File: lenet_cwnet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.conv1(x))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)
        return (out, F.log_softmax(out))
Example #12
Source File: model.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.conv1(x))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)
        return (out)
Example #13
Source File: lenet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.conv1(x))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)
        return (out,F.log_softmax(out))
Example #14
Source File: lenet_cwnet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)
Example #15
Source File: lenet_cwnet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.conv1(x))
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv3(out))
        out = F.relu(self.conv4(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)
        return (out, F.log_softmax(out))
Example #16
Source File: small_model.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        # x = self.conv3(x)
        x = x.view(x.size(0),-1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return (x, F.log_softmax(x))
Example #17
Source File: model.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)
Example #18
Source File: pnasnet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        y1 = self.sep_conv1(x)
        y2 = F.max_pool2d(x, kernel_size=3, stride=self.stride, padding=1)
        if self.stride==2:
            y2 = self.bn1(self.conv1(y2))
        return F.relu(y1+y2)
Example #19
Source File: resnext.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out
Example #20
Source File: senet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.bn1(x))
        shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
        out = self.conv1(out)
        out = self.conv2(F.relu(self.bn2(out)))

        # Squeeze
        w = F.avg_pool2d(out, out.size(2))
        w = F.relu(self.fc1(w))
        w = F.sigmoid(self.fc2(w))
        # Excitation
        out = out * w

        out += shortcut
        return out
Example #21
Source File: model.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.conv1(x))
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv3(out))
        out = F.relu(self.conv4(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)
        return (out)
Example #22
Source File: small_model.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        #x = self.conv1(x)
        #x = self.conv2(x)
        # x = self.conv3(x)
        x = x.view(x.size(0),-1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return (x, F.log_softmax(x))
Example #23
Source File: small_model.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        # x = self.conv3(x)
        x = x.view(x.size(0),-1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return (x, F.log_softmax(x))
Example #24
Source File: shufflenet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out
Example #25
Source File: shufflenet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.shuffle1(out)
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        res = self.shortcut(x)
        out = F.relu(torch.cat([out,res], 1)) if self.stride==2 else F.relu(out+res)
        return out
Example #26
Source File: lenet_cwnet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.conv1(x))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)
        return (out, F.log_softmax(out))
Example #27
Source File: lenet_cwnet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)
Example #28
Source File: lenet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = F.relu(self.conv1(x))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)
        return (out,F.log_softmax(out))
Example #29
Source File: densenet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = self.conv1(x)
        out = self.trans1(self.dense1(out))
        out = self.trans2(self.dense2(out))
        out = self.trans3(self.dense3(out))
        out = self.dense4(out)
        out = F.avg_pool2d(F.relu(self.bn(out)), 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return (out,F.log_softmax(out))
Example #30
Source File: densenet.py    From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def forward(self, x):
        out = self.conv(F.relu(self.bn(x)))
        out = F.avg_pool2d(out, 2)
        return out