This resource is using open-source code maintained in github (see the quick-start-guide section) and available for download from NGC
The nnU-Net ("no-new-Net") refers to a robust and self-adapting framework for U-Net based medical image segmentation. This repository contains a nnU-Net implementation as described in the paper: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation.
The differences between this nnU-net and the original model are:
This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
The nnU-Net allows training two types of networks: 2D U-Net and 3D U-Net to perform semantic segmentation of 2D or 3D images, with high accuracy and performance.
The following figure shows the architecture of the 3D U-Net model and its different components. U-Net is composed of a contractive and an expanding path, that aims at building a bottleneck in its centermost part through a combination of convolution, instance norm, and leaky ReLU operations. After this bottleneck, the image is reconstructed through a combination of convolutions and upsampling. Skip connections are added with the goal of helping the backward flow of gradients to improve the training.
Figure 1: The 3D U-Net architecture
All convolution blocks in U-Net in both encoder and decoder are using two convolution layers followed by instance normalization and a leaky ReLU nonlinearity. For downsampling, we are using stride convolution whereas transposed convolution is used for upsampling.
All models were trained with the Adam optimizer. For loss function we use the average of cross-entropy and dice coefficient.
Used data augmentation: crop with oversampling the foreground class, mirroring, zoom, Gaussian noise, Gaussian blur, brightness.
The following features are supported by this model:
|Automatic mixed precision (AMP)||Yes|
|Horovod Multi-GPU (NCCL)||Yes|
DALI NVIDIA Data Loading Library (DALI) is a collection of optimized building blocks, and an execution engine, to speed up the pre-processing of the input data for deep learning applications. DALI provides both the performance and the flexibility for accelerating different data pipelines as a single library. This single library can then be integrated into different deep learning training and inference applications. For details, refer to example sources in this repository or refer to the DALI documentation.
Automatic Mixed Precision (AMP) Computation graphs can be modified by TensorFlow during runtime to support mixed precision training, which allows using FP16 training with FP32 master weights. A detailed explanation of mixed precision can be found in the next section.
Multi-GPU training with Horovod Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, refer to the Horovod: Official repository. Our model uses Horovod to implement efficient multi-GPU training with NCCL. For details, refer to example scripts in this repository or refer to the TensorFlow tutorial.
XLA XLA (Accelerated Linear Algebra) is a compiler that can speed up TensorFlow networks by model-specific optimizations i.e. fusing many GPU operations together. Operations fused into a single GPU kernel do not have to use extra memory to store intermediate values by keeping them in GPU registers, thus reducing memory operations and improving performance. For details refer to the TensorFlow documentation.
Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in the half-precision format while storing minimal information in single-precision to keep as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x speedup on the most intense model architectures. Using mixed precision training requires two steps:
This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full mixed precision methodology in your existing TensorFlow model code. AMP enables mixed precision training on NVIDIA Volta, NVIDIA Turing, and NVIDIA Ampere GPU architectures automatically. The TensorFlow framework code makes all necessary model changes internally.
In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.
For information about:
Mixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a loss scaling step must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance efforts. Second, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.
Example nnU-Net scripts for training, inference, and benchmarking from the
scripts/ directory enable mixed precision if the
--amp command line flag is used.
Internally, mixed precision is enabled by setting
keras.mixed_precision policy to
mixed_float16. Additionally, our custom training loop uses a
LossScaleOptimizer wrapper for the optimizer. For more information see the Mixed precision guide.
TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 GPUs for handling the matrix math, also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on NVIDIA Volta GPUs.
TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require a high dynamic range for weights or activations.
For more information, refer to the TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x blog post.
TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.
Deep supervision is a technique that adds auxiliary loss outputs to the U-Net decoder layers. For nnU-Net, we add auxiliary losses to the three latest decoder levels. The final loss is a weighted average of the obtained loss values. Deep supervision can be enabled by adding the
Test time augmentation
Test time augmentation is an inference technique that averages predictions from augmented images with its prediction. As a result, predictions are more accurate, but with the cost of a slower inference process. For nnU-Net, we use all possible flip combinations for image augmenting. Test time augmentation can be enabled by adding the
--tta flag to the training or inference script invocation.
Sliding window inference
During inference, this method replaces an arbitrary resolution input image with a batch of overlapping windows, that cover the whole input. After passing this batch through the network a prediction with the original resolution is reassembled. Predicted values inside overlapped regions are obtained from a weighted average.
Overlap ratio and weights for the average (i.e. blending mode) can be adjusted with the