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Prediction

In addition to analyzing the IOU, ACC and Kappa, we can also check the segmentation effect of some specific samples, and inspire further optimization ideas from Bad Case.

The predict.py script is specially used to visualize prediction cases. The command format is as follows:

python predict.py \
       --config configs/quick_start/bisenet_optic_disc_512x512_1k.yml \
       --model_path output/iter_1000/model.pdparams \
       --image_path dataset/optic_disc_seg/JPEGImages/H0003.jpg \
       --save_dir output/result

Among them, image_path can also be a directory. At this time, all the images in the directory will be predicted and the visualization results will be saved.

Similarly, you can use --aug_pred to turn on multi-scale flip prediction, and --is_slide to turn on sliding window prediction.

1.Prepare Dataset

  • When performing prediction, only the original image is needed. You should prepare the contents of test.txt as follows:

    images/image1.jpg
images/image2.jpg
...

  • When calling predict.py for visualization, annotated images can be included in the file list. When predicting, the model will automatically ignore the annotated images given in the file list. Therefore, you can also directly use the training and validating datasets to do predictions. In other words, if the content of your train.txt is as follows：

    images/image1.jpg labels/label1.png
images/image2.jpg labels/label2.png
...
  • At this point, you can specify image_list as train.txt and image_dir as the directory where the training data is located when predicting. The robustness of PaddleSeg allows you to do this, and the output will be the prediction result of the original training data.

2.API

Parameter Analysis of Forecast API

paddleseg.core.predict(
                    model,
                    model_path,
                    transforms,
                    image_list,
                    image_dir=None,
                    save_dir='output',
                    aug_pred=False,
                    scales=1.0,
                    flip_horizontal=True,
                    flip_vertical=False,
                    is_slide=False,
                    stride=None,
                    crop_size=None,
                    custom_color=None
)
  • Parameters
Parameter Type Effection Is Required Default
model nn.Layer Segmentation model Yes -
model_path str The path of parameters in best model Yes -
transforms transform.Compose Preprocess the input image Yes -
image_list list List of image paths to be predicted Yes -
image_dir str The directory of the image path to be predicted No None
save_dir str Output directory No 'output'
aug_pred bool Whether to use multi-scale and flip augmentation for prediction No False
scales list/float Set the zoom factor, take effect when aug_pred is True No 1.0
flip_horizontal bool Whether to use horizontal flip, take effect when aug_eval is True No True
flip_vertical bool Whether to use vertical flip, take effect when aug_eval is True No False
is_slide bool Whether to evaluate through a sliding window No False
stride tuple/list Set the width and height of the sliding window, effective when is_slide is True No None
crop_size tuple/list Set the width and height of the crop of the sliding window, which takes effect when is_slide is True No None
custom_color list Set custom segmentation prediction colors,len(custom_color) = 3 * (pixel classes) No Default color map

Import the API interface and start predicting.

from paddleseg.core import predict
predict(
        model,
        model_path='output/best_model/model.pdparams',# Model path
        transforms=transforms, # Transform.Compose， Preprocess the input image
        image_list=image_list, # List of image paths to be predicted。
        image_dir=image_dir, # The directory where the picture to be predicted is located
        save_dir='output/results' # Output path
    )

3.Instruction of File Structure

If you don't specify the output location, added_prediction and pseudo_color_prediction will be generated under the default folder output/results, which store the results of the pseudo map and blended prediction respectively.

output/result
    |
    |--added_prediction
    |  |--image1.jpg
    |  |--image2.jpg
    |  |--...
    |
    |--pseudo_color_prediction
    |  |--image1.jpg
    |  |--image2.jpg
    |  |--...