problem 4

hw3/saliency_map.py

@@ -0,0 +1,125 @@
+#!/usr/bin/env python
+# -- coding: utf-8 --
+
+import sys, os
+import argparse
+from keras.models import load_model
+from termcolor import colored
+from termcolor import cprint
+import keras.backend as K
+from utils import *
+import numpy as np
+import matplotlib.pyplot as plt
+
+base_dir = './'
+img_dir = os.path.join(base_dir, 'image')
+if not os.path.exists(img_dir):
+ os.makedirs(img_dir)
+cmap_dir = os.path.join(img_dir, 'cmap')
+if not os.path.exists(cmap_dir):
+ os.makedirs(cmap_dir)
+partial_see_dir = os.path.join(img_dir,'partial_see')
+if not os.path.exists(partial_see_dir):
+ os.makedirs(partial_see_dir)
+origin_dir = os.path.join(img_dir,'origin')
+if not os.path.exists(origin_dir):
+ os.makedirs(origin_dir)
+
+def read_data(filename, height=48, width=48):
+ try:
+ print('Loading X.npy & Y.npy')
+ X = np.load('X.npy')
+ Y = np.load('Y.npy')
+ except:
+ with open(filename, "r+") as f:
+ line = f.read().strip().replace(',', ' ').split('\n')[1:]
+ raw_data = ' '.join(line)
+ length = width*height+1 #1 is for label
+ data = np.array(raw_data.split()).astype('float').reshape(-1, length)
+ X = data[:, 1:]
+ Y = data[:, 0]
+ # Change data into CNN format
+ X = X.reshape(-1, height, width, 1)
+ Y = Y.reshape(-1, 1)
+ print('Saving X.npy & Y.npy')
+ np.save('X.npy', X) # (-1, height, width, 1)
+ np.save('Y.npy', Y) # (-1, 1)
+ return X, Y
+
+def main():
+ parser = argparse.ArgumentParser(prog='saliency_map.py',
+ description='ML-Assignment3 visualize attention heat map.')
+ parser.add_argument('--model', type=str, metavar='<#model>', required=True)
+ parser.add_argument('--data', type=str, metavar='<#data>', required=True)
+ args = parser.parse_args()
+ data_name = args.data
+ model_name = args.model
+
+ emotion_classifier = load_model(model_name)
+
+ print(colored("Loaded model from {}".format(model_name), 'yellow', attrs=['bold']))
+ _X, Y = read_data(data_name)
+ X = _X / 255
+ _X = _X.astype('int')
+ Y = Y.squeeze()
+
+ input_img = emotion_classifier.input
+ img_ids = [-3002, -3004, -3005, -3006, -3009, -3010]
+
+ for idx in img_ids:
+ val_proba = emotion_classifier.predict(X[idx].reshape(-1, 48, 48, 1))
+ pred = val_proba.argmax(axis=-1)
+ target = K.mean(emotion_classifier.output[:, pred])
+ grads = K.gradients(target, input_img)[0]
+ fn = K.function([input_img, K.learning_phase()], [grads])
+
+ val_grads = fn([X[idx].reshape(-1, 48, 48, 1), 0])[0].reshape(48, 48, -1)
+
+ val_grads *= -1
+ val_grads = np.max(np.abs(val_grads), axis=-1, keepdims=True)
+
+ # normalize
+ val_grads = (val_grads - np.mean(val_grads)) / (np.std(val_grads) + 1e-30)
+ val_grads *= 0.1
+
+ # clip to [0, 1]
+ val_grads += 0.5
+ val_grads = np.clip(val_grads, 0, 1)
+
+ # scale to [0, 1]
+ val_grads /= np.max(val_grads)
+
+ heatmap = val_grads.reshape(48, 48)
+
+ print('ID: {}, Truth: {}, Prediction: {}'.format(idx, Y[idx], pred))
+ # show original image
+ plt.figure()
+ plt.imshow(_X[idx].reshape(48, 48), cmap='gray')
+ plt.colorbar()
+ plt.tight_layout()
+ fig = plt.gcf()
+ plt.draw()
+ fig.savefig(os.path.join(origin_dir, '{}.png'.format(idx)), dpi=100)
+
+ thres = 0.5
+ see = _X[idx].reshape(48, 48)
+ see[np.where(heatmap <= thres)] = np.mean(see)
+
+ plt.figure()
+ plt.imshow(heatmap, cmap=plt.cm.jet)
+ plt.colorbar()
+ plt.tight_layout()
+ fig = plt.gcf()
+ plt.draw()
+ fig.savefig(os.path.join(cmap_dir, '{}.png'.format(idx)), dpi=100)
+
+ plt.figure()
+ plt.imshow(see, cmap='gray')
+ plt.colorbar()
+ plt.tight_layout()
+ fig = plt.gcf()
+ plt.draw()
+ fig.savefig(os.path.join(partial_see_dir, '{}.png'.format(idx)), dpi=100)
+
+if __name__ == "__main__":
+ main()