The V-Net model for Tensorflow, called V-Net_Medical_TF is a convolutional neural network for 3D image segmentation. This repository contains a V-Net implementation and is based on the paper V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation, with small alterations to support a new dataset for Hippocampus segmentation.
This implementation differs from the original in the following ways:
- Convolution filters are 3x3x3 instead of 5x5x5 to increase performance without negatively affecting the accuracy
- The number of upsample/downsample levels is reduced to 3 to accommodate the different input size
- PReLU activation has been substituted by ReLU to increase performance without negatively affecting the accuracy
This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results 2.2x 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.
V-Net was first introduced by Fausto Milletari, Nassir Navab, Seyed-Ahmad Ahmadi in the paper: V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. V-Net allows for seamless segmentation of 3D images, with high accuracy and performance, and can be adapted to solve many different segmentation problems.
The following figure shows the construction of the standard V-Net model and its different components. V-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 and downsampling. 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 in order to improve the training.
Figure 1. VNet architecture
V-Net consists of a contractive (left-side) and expanding (right-side) path. It repeatedly applies unpadded convolutions followed by max pooling for downsampling. Every step in the expanding path consists of an upsampling of the feature maps and a concatenation with the correspondingly cropped feature map from the contractive path.
The following performance optimizations were implemented in this model:
- XLA support.
- Reduced size of convolutional filters to 3x3x3
- ReLU activation used instead of PReLU
- Batchnorm used for training
Feature support matrix
The following features are supported by this model.
|Horovod Multi-GPU (NCCL)
|Automatic Mixed Precision (AMP)
The following features were implemented in this model:
- Data-parallel multi-GPU training with Horovod.
- Mixed precision support with TensorFlow Automatic Mixed Precision (TF-AMP), which enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable.
- Tensor Core operations to maximize throughput using NVIDIA Volta GPUs.
- Multi-GPU training with Horovod
Our model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the TensorFlow tutorial.
- Automatic Mixed Precision (AMP)
Enables mixed precision training without any changes to the code-base by performing automatic graph rewrites and loss scaling controlled by an environmental variable.
Mixed precision training
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 half-precision format while storing minimal information in single-precision to retain 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 overall speedup on the most arithmetically intense model architectures. Using mixed precision training previously required two steps:
- Porting the model to use the FP16 data type where appropriate.
- Adding loss scaling to preserve small gradient values.
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 Volta and Turing GPUs 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.
- How to train using mixed precision, see the Mixed Precision Training paper and Training With Mixed Precision documentation.
- Techniques used for mixed precision training, see the Mixed-Precision Training of Deep Neural Networks blog.
- How to access and enable AMP for TensorFlow, see Using TF-AMP from the TensorFlow User Guide.
Enabling mixed precision
In order to enable mixed precision training, the following environment variables must be defined with the correct value before the training starts:
Exporting these variables ensures that loss scaling is performed correctly and automatically.
By supplying the
--amp flag to the
main.py script while training in FP32, the following variables are set to their correct value for mixed precision training inside the
LOGGER.log("TF AMP is activated - Experimental Feature")
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
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 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 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.