Pseudo-3D CNN networks in PyTorch.
This repository contains my own (unofficial) implementation of Pseudo-3D convolutional networks from the paper "Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks" (https://arxiv.org/abs/1711.10305).
Precise description of the architecture can be found in the paper. Detailed description of the P3D blocks can be found in the paper as well.
Additionally, I have implemented 3D version of (CBAM - Bottleneck Attention Module) from "Convolutional Block Attention Module" paper. (https://openaccess.thecvf.com/content_ECCV_2018/html/Sanghyun_Woo_Convolutional_Block_Attention_ECCV_2018_paper.html).
This implementation extends original CBAM module to 3D. In this case, the spatial attention is computed along the temporal dimension (dim=2). Then, mean and max features are concatenated in the filter dimension (dim=1), like in the original CBAM module. It is because MRI scans are 1-channel 3D volumes, and important features can be extracted from the temporal dimension in this case.
Repository contains implementation of P3D blocks (type A, B, and C) and P3D ResNet-like networks. To use them, you can
import them from p3d_resnet.py file and use P3DResNet class.
Model size is parametrized - you can choose between
every ResNet size using num_blocks_per_stage parameter. Default is (3, 4, 6, 3), which corresponds to the ResNet-50
architecture. For ResNet-101, use (3, 4, 23, 3). For ResNet-152, use (3, 8, 36, 3).
You can also build your own P3D network using P3DBlockA, P3DBlockB, and P3DBlockC classes from src/blocks.py file.
This file also contains implementation of Attention module (AttentionBlock3D class).
Blocks and P3D models are implemented using PyTorch's nn.Module class, so you can use them as any other PyTorch
module. Parameters of these classes are described in the docstrings.
Repository also contains tests in tests directory. You can run them using pytest command.
Repository is compatible with Python 3.10 and PyTorch 2.0.1.