# Wandb Feature Map Visualization
MMSegmentation 1.x provides backend support for Weights & Biases to facilitate visualization and management of project code results.
## Wandb Configuration
Install Weights & Biases following [official instructions](https://docs.wandb.ai/quickstart) e.g.
```shell
pip install wandb
wandb login
```
Add `WandbVisBackend` in `vis_backend` of `visualizer` in `default_runtime.py` config file:
```python
vis_backends=[dict(type='LocalVisBackend'),
dict(type='TensorboardVisBackend'),
dict(type='WandbVisBackend')]
```
## Examining feature map visualization in Wandb
`SegLocalVisualizer` is child class inherits from `Visualizer` in MMEngine and works for MMSegmentation visualization, for more details about `Visualizer` please refer to [visualization tutorial](https://github.com/open-mmlab/mmengine/blob/main/docs/en/advanced_tutorials/visualization.md) in MMEngine.
Here is an example about `SegLocalVisualizer`, first you may download example data below by following commands:
<div align=center>
<img src="https://user-images.githubusercontent.com/24582831/189833109-eddad58f-f777-4fc0-b98a-6bd429143b06.png" width="70%"/>
</div>
```shell
wget https://user-images.githubusercontent.com/24582831/189833109-eddad58f-f777-4fc0-b98a-6bd429143b06.png --output-document aachen_000000_000019_leftImg8bit.png
wget https://user-images.githubusercontent.com/24582831/189833143-15f60f8a-4d1e-4cbb-a6e7-5e2233869fac.png --output-document aachen_000000_000019_gtFine_labelTrainIds.png
wget https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth
```
```python
# Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser
from typing import Type
import mmcv
import torch
import torch.nn as nn
from mmengine.model import revert_sync_batchnorm
from mmengine.structures import PixelData
from mmseg.apis import inference_model, init_model
from mmseg.structures import SegDataSample
from mmseg.utils import register_all_modules
from mmseg.visualization import SegLocalVisualizer
class Recorder:
"""record the forward output feature map and save to data_buffer."""
def __init__(self) -> None:
self.data_buffer = list()
def __enter__(self, ):
self._data_buffer = list()
def record_data_hook(self, model: nn.Module, input: Type, output: Type):
self.data_buffer.append(output)
def __exit__(self, *args, **kwargs):
pass
def visualize(args, model, recorder, result):
seg_visualizer = SegLocalVisualizer(
vis_backends=[dict(type='WandbVisBackend')],
save_dir='temp_dir',
alpha=0.5)
seg_visualizer.dataset_meta = dict(
classes=model.dataset_meta['classes'],
palette=model.dataset_meta['palette'])
image = mmcv.imread(args.img, 'color')
seg_visualizer.add_datasample(
name='predict',
image=image,
data_sample=result,
draw_gt=False,
draw_pred=True,
wait_time=0,
out_file=None,
show=False)
# add feature map to wandb visualizer
for i in range(len(recorder.data_buffer)):
feature = recorder.data_buffer[i][0] # remove the batch
drawn_img = seg_visualizer.draw_featmap(
feature, image, channel_reduction='select_max')
seg_visualizer.add_image(f'feature_map{i}', drawn_img)
if args.gt_mask:
sem_seg = mmcv.imread(args.gt_mask, 'unchanged')
sem_seg = torch.from_numpy(sem_seg)
gt_mask = dict(data=sem_seg)
gt_mask = PixelData(**gt_mask)
data_sample = SegDataSample()
data_sample.gt_sem_seg = gt_mask
seg_visualizer.add_datasample(
name='gt_mask',
image=image,
data_sample=data_sample,
draw_gt=True,
draw_pred=False,
wait_time=0,
out_file=None,
show=False)
seg_visualizer.add_image('image', image)
def main():
parser = ArgumentParser(
description='Draw the Feature Map During Inference')
parser.add_argument('img', help='Image file')
parser.add_argument('config', help='Config file')
parser.add_argument('checkpoint', help='Checkpoint file')
parser.add_argument('--gt_mask', default=None, help='Path of gt mask file')
parser.add_argument('--out-file', default=None, help='Path to output file')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference')
parser.add_argument(
'--opacity',
type=float,
default=0.5,
help='Opacity of painted segmentation map. In (0, 1] range.')
parser.add_argument(
'--title', default='result', help='The image identifier.')
args = parser.parse_args()
register_all_modules()
# build the model from a config file and a checkpoint file
model = init_model(args.config, args.checkpoint, device=args.device)
if args.device == 'cpu':
model = revert_sync_batchnorm(model)
# show all named module in the model and use it in source list below
for name, module in model.named_modules():
print(name)
source = [
'decode_head.fusion.stages.0.query_project.activate',
'decode_head.context.stages.0.key_project.activate',
'decode_head.context.bottleneck.activate'
]
source = dict.fromkeys(source)
count = 0
recorder = Recorder()
# registry the forward hook
for name, module in model.named_modules():
if name in source:
count += 1
module.register_forward_hook(recorder.record_data_hook)
if count == len(source):
break
with recorder:
# test a single image, and record feature map to data_buffer
result = inference_model(model, args.img)
visualize(args, model, recorder, result)
if __name__ == '__main__':
main()
```
Save the above code as feature_map_visual.py and execute the following code in terminal
```shell
python feature_map_visual.py ${image} ${config} ${checkpoint} [optional args]
```
e.g
```shell
python feature_map_visual.py \
aachen_000000_000019_leftImg8bit.png \
configs/ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.py \
ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth \
--gt_mask aachen_000000_000019_gtFine_labelTrainIds.png
```
The visualized image result and its corresponding feature map will appear in the wandb account.
<div align=center>
<img src="https://user-images.githubusercontent.com/76149310/217520321-647f5bf9-eef2-446d-a9e8-5ca7b621d500.png">
</div>