Upload resnet_gn.py

resnet_gn.py

@@ -0,0 +1,375 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+import math
+from functools import partial
+
+__all__ = [
+    'ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
+    'resnet152', 'resnet200'
+]
+
+class FilterResponseNormNd(nn.Module):
+
+    def __init__(self, ndim, num_features, eps=1e-6,
+                 learnable_eps=False):
+        """
+        Input Variables:
+        ----------------
+            ndim: An integer indicating the number of dimensions of the expected input tensor.
+            num_features: An integer indicating the number of input feature dimensions.
+            eps: A scalar constant or learnable variable.
+            learnable_eps: A bool value indicating whether the eps is learnable.
+        """
+        assert ndim in [3, 4, 5], \
+            'FilterResponseNorm only supports 3d, 4d or 5d inputs.'
+        super(FilterResponseNormNd, self).__init__()
+        shape = (1, num_features) + (1,) * (ndim - 2)
+        self.eps = nn.Parameter(torch.ones(*shape) * eps)
+        if not learnable_eps:
+            self.eps.requires_grad_(False)
+        self.gamma = nn.Parameter(torch.Tensor(*shape))
+        self.beta = nn.Parameter(torch.Tensor(*shape))
+        self.tau = nn.Parameter(torch.Tensor(*shape))
+        self.reset_parameters()
+
+    def forward(self, x):
+        avg_dims = tuple(range(2, x.dim())) # (2, 3)
+        nu2 = torch.pow(x, 2).mean(dim=avg_dims, keepdim=True)
+        x = x * torch.rsqrt(nu2 + torch.abs(self.eps))
+        return torch.max(self.gamma * x + self.beta, self.tau)
+
+    def reset_parameters(self):
+        nn.init.ones_(self.gamma)
+        nn.init.zeros_(self.beta)
+        nn.init.zeros_(self.tau)
+
+def conv3x3x3(in_planes, out_planes, stride=1):
+    # 3x3x3 convolution with padding
+    return nn.Conv3d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=1,
+        bias=False)
+
+
+def downsample_basic_block(x, planes, stride):
+    out = F.avg_pool3d(x, kernel_size=1, stride=stride)
+    zero_pads = torch.Tensor(
+        out.size(0), planes - out.size(1), out.size(2), out.size(3),
+        out.size(4)).zero_()
+    if isinstance(out.data, torch.cuda.FloatTensor):
+        zero_pads = zero_pads.cuda()
+
+    out = Variable(torch.cat([out.data, zero_pads], dim=1))
+
+    return out
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3x3(inplanes, planes, stride)
+        self.gn1 = nn.GroupNorm(32,planes)
+        #self.bn1 = nn.BatchNorm3d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3x3(planes, planes)
+        #self.bn2 = nn.BatchNorm3d(planes)
+        self.gn2 = nn.GroupNorm(32,planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        #out = self.bn1(out)
+        out = self.gn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        #out = self.bn2(out)
+        out = self.gn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, bias=False)
+        #self.bn1 = nn.BatchNorm3d(planes)
+        self.gn1 = nn.GroupNorm(32,planes)
+        self.conv2 = nn.Conv3d(
+            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        #self.bn2 = nn.BatchNorm3d(planes)
+        self.gn2 = nn.GroupNorm(32,planes)
+        self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=1, bias=False)
+        #self.bn3 = nn.BatchNorm3d(planes * 4)
+        self.gn3 = nn.GroupNorm(32,planes*4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        #out = self.bn1(out)
+        out = self.gn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        #out = self.bn2(out)
+        out = self.gn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        #out = self.bn3(out)
+        out = self.gn3(out)
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+class MLP(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        output_dim: int,
+        num_layers: int,
+        sigmoid_output: bool = False,
+    ) -> None:
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(
+            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+        )
+        self.sigmoid_output = sigmoid_output
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        if self.sigmoid_output:
+            x = F.sigmoid(x)
+        return x
+
+class ResNet(nn.Module):
+
+    def __init__(self,
+                 block,
+                 layers,
+                 sample_size,
+                 sample_duration,
+                 shortcut_type='B',
+                 num_classes=400):
+        self.num_classes = num_classes
+        self.inplanes = 64
+        super(ResNet, self).__init__()
+        self.conv1 = nn.Conv3d(
+            1,
+            64,
+            kernel_size=7,
+            stride=(1, 2, 2),
+            padding=(3, 3, 3),
+            bias=False)
+        #self.bn1 = nn.BatchNorm3d(64)
+        self.gn1 = nn.GroupNorm(32,64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0], shortcut_type)
+        self.layer2 = self._make_layer(
+            block, 128, layers[1], shortcut_type, stride=2)
+        self.layer3 = self._make_layer(
+            block, 256, layers[2], shortcut_type, stride=2)
+        self.layer4 = self._make_layer(
+            block, 512, layers[3], shortcut_type, stride=2)
+        last_duration = int(math.ceil(sample_duration / 16))
+        last_size = int(math.ceil(sample_size / 32))
+        self.avgpool = nn.AvgPool3d(
+            (last_duration, last_size, last_size), stride=1)
+        # self.avgpool = nn.AvgPool3d(
+        #     (4, 2, 2), stride=1)
+        #self.fc = nn.Linear(81920, num_classes)
+        self.classfily = MLP(81920, 256, self.num_classes, 2, sigmoid_output=False)
+
+        # for m in self.modules():
+        #     if isinstance(m, nn.Conv3d):
+        #         m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out')
+        #     elif isinstance(m, nn.BatchNorm3d):
+        #         m.weight.data.fill_(1)
+        #         m.bias.data.zero_()
+        for m in self.modules():
+            if isinstance(m, nn.Conv3d):
+                m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out')
+            elif isinstance(m, nn.GroupNorm):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+
+    def _make_layer(self, block, planes, blocks, shortcut_type, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            if shortcut_type == 'A':
+                downsample = partial(
+                    downsample_basic_block,
+                    planes=planes * block.expansion,
+                    stride=stride)
+            else:
+                downsample = nn.Sequential(
+                    nn.Conv3d(
+                        self.inplanes,
+                        planes * block.expansion,
+                        kernel_size=1,
+                        stride=stride,
+                        bias=False), nn.GroupNorm(32,planes * block.expansion))
+                # downsample = nn.Sequential(
+                #     nn.Conv3d(
+                #         self.inplanes,
+                #         planes * block.expansion,
+                #         kernel_size=1,
+                #         stride=stride,
+                #         bias=False), nn.BatchNorm3d(planes * block.expansion))
+
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+
+    def forward(self, x):
+        x = self.conv1(x)
+        #x = self.bn1(x)
+        x = self.gn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+
+        x = x.view(x.size(0), -1)
+        #x = self.fc(x)
+        self.feature = x
+        x = self.classfily(x)
+        if self.num_classes==1:
+            x = F.sigmoid(x)
+        return x
+
+
+# def initialize_weights(self):
+#         # print(self.modules())
+#
+#     for m in self.modules():
+#         if isinstance(m, nn.Linear):
+#                 # print(m.weight.data.type())
+#                 # input()
+#                 # m.weight.data.fill_(1.0)
+#             nn.init.kaiming_normal_(m.weight,a=0, mode='fan_in', nonlinearity='relu')
+#             print(m.weight)
+
+def weights_init(m):
+    classname = m.__class__.__name__
+    if classname.find('Conv2d') != -1:
+        nn.init.xavier_normal_(m.weight.data)
+        nn.init.constant_(m.bias.data, 0.0)
+    elif classname.find('Linear') != -1:
+        nn.init.xavier_normal_(m.weight)
+        nn.init.constant_(m.bias, 0.0)
+
+def get_fine_tuning_parameters(model, ft_begin_index):
+    if ft_begin_index == 0:
+        return model.parameters()
+
+    ft_module_names = []
+    for i in range(ft_begin_index, 5):
+        ft_module_names.append('layer{}'.format(i))
+    ft_module_names.append('fc')
+
+    parameters = []
+    for k, v in model.named_parameters():
+        for ft_module in ft_module_names:
+            if ft_module in k:
+                parameters.append({'params': v})
+                break
+        else:
+            parameters.append({'params': v, 'lr': 0.0})
+
+    return parameters
+
+
+def resnet10(**kwargs):
+    """Constructs a ResNet-18 model.
+    """
+    model = ResNet(BasicBlock, [1, 1, 1, 1], **kwargs)
+    return model
+
+
+def resnet18(**kwargs):
+    """Constructs a ResNet-18 model.
+    """
+    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
+    return model
+
+
+def resnet34(**kwargs):
+    """Constructs a ResNet-34 model.
+    """
+    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
+    return model
+
+
+def resnet50(**kwargs):
+    """Constructs a ResNet-50 model.
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
+    #model.apply(weights_init)
+    return model
+
+
+def resnet101(**kwargs):
+    """Constructs a ResNet-101 model.
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
+    # model.apply(weights_init)
+    return model
+
+
+def resnet152(**kwargs):
+    """Constructs a ResNet-101 model.
+    """
+    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
+    return model
+
+
+def resnet200(**kwargs):
+    """Constructs a ResNet-101 model.
+    """
+    model = ResNet(Bottleneck, [3, 24, 36, 3], **kwargs)
+    # model.apply(weights_init)
+    return model