我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用torch.nn.UpsamplingBilinear2d()。
def assureRatio(img): """Ensure imgH <= imgW.""" b, c, h, w = img.size() if h > w: main = nn.UpsamplingBilinear2d(size=(h, h), scale_factor=None) img = main(img) return img
def __init__(self, in_size, out_size, is_deconv): super(unetUp, self).__init__() self.conv = unetConv2(in_size, out_size, False) if is_deconv: self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=2, stride=2) else: self.up = nn.UpsamplingBilinear2d(scale_factor=2)
def __init__(self, joint_index, mpii, batch_size): super(JointModel, self).__init__() self.joint_index = joint_index self.heatmap_size = mpii.heatmap_size self.max_people = mpii.max_people self.max_joints = mpii.max_joints ndf = 64 self.conv0 = nn.Conv2d(mpii.image_num_components, ndf, 11, stride=2) self.conv1 = nn.Conv2d(ndf, ndf * 2, 9, stride=2) self.conv2 = nn.Conv2d(ndf * 2, ndf * 4, 7, stride=2) self.conv3 = nn.Conv2d(ndf * 4, ndf * 8, 5, stride=2) self.fc0_size = 512 * 3 * 3 self.fc0 = nn.Linear(self.fc0_size, self.heatmap_size * self.heatmap_size) self.relu = nn.ReLU(inplace=True) self.tanh = nn.Tanh() self.softmax = nn.Softmax() self.loss = nn.BCELoss().cuda() self.images = Variable(torch.FloatTensor(batch_size, mpii.image_num_components, mpii.image_size, mpii.image_size)).cuda() self.labels = Variable(torch.FloatTensor(batch_size, self.heatmap_size, self.heatmap_size)).cuda() self.scale_heatmap = nn.UpsamplingBilinear2d(scale_factor=4)
def __init__(self, block1, block2, batch_size): super(Model, self).__init__() self.batch_size=batch_size # Layers for Depth Estimation self.conv1 = nn.Conv2d(3, 64, kernel_size = 7, stride=2, padding=4) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.max_pool = nn.MaxPool2d(3,stride=2) self.proj_layer1 = self.make_proj_layer(block1, 64 , d1 = 64, d2 = 256, stride = 1) self.skip_layer1_1 = self.make_skip_layer(block1, 256, d1 = 64, d2 = 256, stride=1) self.skip_layer1_2 = self.make_skip_layer(block1, 256, d1 = 64, d2 = 256, stride=1) self.proj_layer2 = self.make_proj_layer(block1, 256 , d1 = 128, d2 = 512, stride = 2) self.skip_layer2_1 = self.make_skip_layer(block1, 512, d1 = 128, d2 = 512) self.skip_layer2_2 = self.make_skip_layer(block1, 512, d1 = 128, d2 = 512) self.skip_layer2_3 = self.make_skip_layer(block1, 512, d1 = 128, d2 = 512) self.proj_layer3 = self.make_proj_layer(block1, 512 , d1 = 256, d2 = 1024, stride=2) self.skip_layer3_1 = self.make_skip_layer(block1, 1024, d1 = 256, d2 = 1024) self.skip_layer3_2 = self.make_skip_layer(block1, 1024, d1 = 256, d2 = 1024) self.skip_layer3_3 = self.make_skip_layer(block1, 1024, d1 = 256, d2 = 1024) self.skip_layer3_4 = self.make_skip_layer(block1, 1024, d1 = 256, d2 = 1024) self.skip_layer3_5 = self.make_skip_layer(block1, 1024, d1 = 256, d2 = 1024) self.proj_layer4 = self.make_proj_layer(block1, 1024 , d1 = 512, d2 = 2048, stride=2) self.skip_layer4_1 = self.make_skip_layer(block1, 2048, d1 = 512, d2 = 2048) self.skip_layer4_2 = self.make_skip_layer(block1, 2048, d1 = 512, d2 = 2048) self.conv2 = nn.Conv2d(2048,1024,1) self.bn2 = nn.BatchNorm2d(1024) self.up_conv1 = self.make_up_conv_layer(block2, 1024, 512, self.batch_size) self.up_conv2 = self.make_up_conv_layer(block2, 512, 256, self.batch_size) self.up_conv3 = self.make_up_conv_layer(block2, 256, 128, self.batch_size) self.up_conv4 = self.make_up_conv_layer(block2, 128, 64, self.batch_size) self.conv3 = nn.Conv2d(64,1,3, padding=1) # Layers for Semantic Segmentation self.up_conv5 = self.make_up_conv_layer(block2,128 ,64 ,self.batch_size) self.conv4 = nn.Conv2d(64,48,3,padding=1) self.bn4 = nn.BatchNorm2d(48) self.conv5 = nn.Conv2d(48,38,3,padding=1) self.bn5 = nn.BatchNorm2d(38) self.dropout = nn.Dropout2d(p=1) self.upsample = nn.UpsamplingBilinear2d(size = (480,640))
