model=Unet()inp=torch.rand(10,1,224,224)outp=model(inp)print(outp.shape)==>torch.Size([10,1,224,224]) 先把上采样和两个卷积层分别构建好,供Unet模型构建中重复使用。然后模型的输出和输入是相同的尺寸,说明模型可以运行。 参考博客:
AI代码解释 """ Full assembly of the parts to form the complete network """Refer https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_model.py"""importtorch.nn.functionalasFfrom unet_partsimport*classUNet(nn.Module):def__init__(self,n_channels,n_classes,bilinear=False):super(UN...
train\label文件夹:训练数据集的语义分割标签 【model】:定义unet网络结构 unet-model.py:模型主干网络 unet-part.py:组测unet网络结构的若干卷积模块 【utils】:定义工具文件 dataset.py:加载data文件夹下的图片和标签 unet: best_model.pth:模型训练后保存的权重文件 train.py:模型训练 predict.py:模型预测 requi...
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使用命令python unet_model.py,如果没有错误,你会得到如下结果: AI检测代码解析 UNet( (inc): DoubleConv( (double_conv): Sequential( (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1)) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ...
model = Unet() inp = torch.rand(10,1,224,224) outp = model(inp)print(outp.shape) ==> torch.Size([10,1,224,224]) 先把上采样和两个卷积层分别构建好,供Unet模型构建中重复使用。然后模型的输出和输入是相同的尺寸,说明模型可以运行。
(w-target_width)// 2returntensor[:,:,crop_y:crop_y+target_height,crop_x:crop_x+target_width]# 使用示例model=UNet(in_channels=1,out_channels=1)# 输入和输出均为1通道(例如用于灰度图像)input_image=torch.randn(1,1,572,572)# 随机生成一个输入图像output=model(input_image)print(output....
先看注释对网络的解释:UNet2DConditionModelis a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep and returns sample shaped output.(UNet2DConditionModel 是一个条件2D UNet模型,它接受一个有噪声的样本、条件状态和一个时间步长并返回样本形状的输出。) ...
model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])returnmodel Unet的代码实现(pytorch版) """这是根据UNet模型搭建出的一个基本网络结构 输入和输出大小是一样的,可以根据需求进行修改"""import torch import torch.nnasnnfromtorch.nn import functionalasF ...
model = _unet( n_classes , get_mobilenet_encoder , input_height=input_height, input_width=input_width ) model.model_name = "mobilenet_unet" return model 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. ...