model = monai.networks.nets.UNet(spatial_dims=2,in_channels=1,out_channels=1, channels=(16, 32, 64, 128, 256),strides=(2, 2, 2, 2),num_res_units=2,).to(device) loss_function = monai.losses.DiceLoss(sigmoid=True) optimizer = torch.optim.Adam(model.parameters(), 1e-3) model...
importloggingimportosimportsysimporttempfilefromglobimportglobimporttorchfromPILimportImagefrommonaiimportconfigfrommonai.dataimportArrayDataset,create_test_image_2d,decollate_batch,DataLoaderfrommonai.inferersimportsliding_window_inferencefrommonai.metricsimportDiceMetricfrommonai.networks.netsimportUNetfrommonai.t...
device = torch.device('cuda'iftorch.cuda.is_available ()else'cpu') model = monai.networks.nets.UNet().to(device) model = nn.DataParallel(model) 通过两种方式可以指定需要使用的GPU,第一种是在代码里设置os.environ, ...
我们将使用卷积神经网络(CNN)作为示例模型。 importmonaifrommonai.networks.netsimportUNet# 创建模型model=UNet(dimensions=2,in_channels=1,out_channels=2,kernel_size=3)print(model)# 打印模型结构 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 这里的UNet是医学影像处理中常见的深度学习模型。 步...
importtorch.nnasnnfrommonai.networks.netsimportUNet# 定义网络结构,使用 UNet 作为示例model=UNet(spatial_dims=2,in_channels=1,out_channels=2,features=(16,32,64,128),) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 步骤5: 训练模型 在这一阶段,我们需要定义损失函数和优化器,并使用训练循环来训练模...
from generative.networks.netsimportDiffusionModelUNet...diffusion=DiffusionModelUNet(spatial_dims=2,in_channels=3,out_channels=3,num_res_blocks=2,num_channels=[256,512,768],attention_levels=[False,True,True],with_cnotallow=True,cross_attention_dim=1024,num_head_channels=[0,512,768],) ...
networks Removed hard-coded spatial_dims in SwinTransformer (#7302) Fixed learnable position_embeddings in PatchEmbeddingBlock (#7564, #7605) Removed memory_pool_limit in TRT config (#7647) Propagated kernel_size to ConvBlocks within AttentionUnet (#7734) Addressed hard-coded activation layer in ...
### 摘要 MONAI是一个专为医学成像领域设计的深度学习框架,它允许开发者在原生的PyTorch环境中构建高效的医学成像训练工作流。凭借其灵活的多维医学成像数据预处理能力和组合及可移植的API,MONAI不仅提高了数据处理的效率与精度,还简化了与现有工作流程的集成过程。通过丰富的代码示例,本文展示了如何利用MONAI的强大功...
https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/nets/unet.py The default Unet module now only uses deconvolution for feature map upsampling. It introduces checkerboard artefacts when you use it as a GAN generator. Can we add torch.Upsample as another option for upsampling?
networksRemoved hard-coded spatial_dims in SwinTransformer (#7302) Fixed learnable position_embeddings in PatchEmbeddingBlock (#7564, #7605) Removed memory_pool_limit in TRT config (#7647) Propagated kernel_size to ConvBlocks within AttentionUnet (#7734) Addressed hard-coded activation layer in ...