EADST

Paddle Class Activation Mapping with PPMA.

Using Class Activation Mapping(CAM) to check the model explainability and draw the heatmap.

After trained the model with PaddleClas, the following code can be used to check the heatmap of the infer image.

import os
from ppma import cam # pip install ppma 
from ppcls.arch import build_model
import cv2

def heatmap(img_path, config, label=None):
    model = build_model(config)
    # print(model) # check the model blocks and layers
    target_layer = model.blocks6[-1] # last layer from last block                       
    cam_extractor = cam.GradCAMPlusPlus(model, target_layer)
    activation_map = cam_extractor(img_path, label=label)   
    cam_image = cam.overlay(img_path, activation_map)   
    cv2.imwrite("res_{}".format(os.path.basename(img_path)), cam_image)
    print("Finished")


if __name__ == "__main__":
    img_path = 'vw.jpg' 
    # config file YAML "Arch" part
    config = {'Arch': {'name': 'PPLCNet_x1_0', 
                       'pretrained': './output/v9c/PPLCNet_x1_0/latest', 
                       'class_num': 110, 
                       'use_ssld': True, 
                       'lr_mult_list': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 
                       'infer_add_softmax': False
                      }
             }
    label = 48 # image gound truth
    heatmap(img_path, config, label)

Reference:

Paddle Model Analysis