attention-module | Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Blo | Machine Learning library
kandi X-RAY | attention-module Summary
kandi X-RAY | attention-module Summary
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
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Top functions reviewed by kandi - BETA
- main function .
- Train the model .
- Validate the model .
- Residual network .
- Create a convolutional layer .
- Compute accuracy .
- Adjust learning rate .
- Compute the logsum of a tensor .
- Update the statistics .
- Save checkpoint .
attention-module Key Features
attention-module Examples and Code Snippets
Lines of Code : 48
License : No License
CopyMIXUP_EPOCH = 50 # 从第50个epoch开始mix_up
……
if epoch > MIXUP_EPOCH:
mix_up_flag = True
else:
mix_up_flag = False
for i, (images, target) in enumerate(train_loader):
# measure data loading time
data_time.up FAParser
│ README.md
│ train.py
│ inference.py
│ preprocess.py
│
└───evaluation: for validation or testing
│ │ F1
│ │ Accuracy
│ │ │ UAS
│ │ └ LAS
│ └ ...
│
└───data:
│ │ tree loaded or structure utils
│ │ 表1 VisDrone2019-DET-Test (不同网络模型下各类目标的平均精度均值)
pedestrain people bicycle car van trunk tricycle Awing-tricycle bus motor mAP0.5
YOLOv3-Tiny 15.52% 15.66% 25.19% 80.21% 43.83% 25.64% 16.75% 1 import numpy as np
import torch
import torch.nn as nn
import dgl
import dgl.function as fn
import dgl.nn as dglnn
from dgl.base import DGLError
from dgl.nn.functional import edge_softmax
class WeightedGATConv(dglnn.GATConv):
"""
This model """
Hypergraph Convolution and Hypergraph Attention
(https://arxiv.org/pdf/1901.08150.pdf).
"""
import dgl
import dgl.mock_sparse as dglsp
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchmetrics.functional import accuracy """
.. _model-transformer:
Transformer as a Graph Neural Network
======================================
**Author**: Zihao Ye, Jinjing Zhou, Qipeng Guo, Quan Gan, Zheng Zhang
.. warning::
The tutorial aims at gaining insights into the paper, w Community Discussions
Trending Discussions on attention-module
QUESTION
Please add a minimum comment on your thoughts so that I can improve my query. Thank you. -)
I'm trying to understand and implement a research work on Triple Attention Learning, which consists on
...ANSWER
Answered 2021-Mar-02 at 00:56When paper introduce they method they said:
The attention modules aim to exploit the relationship between disease labels and (1) diagnosis-specific feature channels, (2) diagnosis-specific locations on images (i.e. the regions of thoracic abnormalities), and (3) diagnosis-specific scales of the feature maps.
(1), (2), (3) corresponding to channel-wise attention, element-wise attention, scale-wise attention
We can tell that element-wise attention is for deal with disease location & weight info, i.e: at each location on image, how likely there is a disease, as it been mention again when paper introduce the element-wise attention：
The element-wise attention learning aims to enhance the sensitivity of feature representations to thoracic abnormal regions, while suppressing the activations when there is no abnormality.
OK, we could easily get location & weight info for one disease, but we have multiple disease:
Since there are multiple thoracic diseases, we choose to estimate an element-wise attention map for each category in this work.
We could store the multiple disease location & weight info by using a tensor A with shape (height, width, number of disease):
The all-category attention map is denoted by A ∈ RH×W×C, where each element aijc is expected to represent the relative importance at location (i, j) for identifying the c-th category of thoracic abnormalities.
And we have linear classifiers for produce a tensor S with same shape as A, this can be interpret as:
At each location on feature maps X(CA), how confident those linear classifiers think there is certain disease at that location
Now we element-wise multiply S and A to get M, i.e we are:
prevent the attention maps from paying unnecessary attention to those location with non-existent labels
So after all those, we get tensor M which tells us:
location & weight info about certain disease that linear classifiers are confident about it
Then if we do global average pooling over M, we get prediction of weight for each disease, add another softmax (or sigmoid) we could get prediction of probability for each disease
Now since we have label and prediction, so, naturally we could minimizing loss function to optimize the model.
ImplementationFollowing code is tested on colab and will show you how to implement channel-wise attention and element-wise attention, and build and training a simple model base on your code with DenseNet121 and without scale-wise attention:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install attention-module
You can use attention-module like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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