import numpy as np
import time
import matplotlib.pyplot as plt
from data_loader import loaddata
from model import MLP
import argparse
import logging
logging.basicConfig(level=logging.DEBUG)
#init config
d0 = 784 #datadimension
d1 = h = 1000 #number of hidden units
d2 = C = 10 #number of classes
#init model
model = MLP(d0, d1, d2)
time.sleep(1)
#load model
parser = argparse.ArgumentParser()
parser.add_argument('--input_dir')
parser.add_argument('--check_point', default = 'model')
args = parser.parse_args()
model.load_checkpoint(args.check_point)
#load img
result = model.test(args.input_dir)
logging.info('Result is {}'.format(result))
for k, r in enumerate(rlist):
salience_map[j][r] = Y[k]
_, _, weights = rf_sal.get_weights(im_data.feature93s[j])
rf_sal_weight += np.mean(weights, axis=0)[:, 1]
rf_sal_weight /= len(im_data.rlists)
ground_truth = cv2.imread(seg_paths[i])[:, :, 0]
ground_truth[ground_truth == 255] = 1
x = salience_map.reshape([-1, height * width]).T
salience_maps.append(x)
ground_truths.append(ground_truth.reshape(-1))
result = mlp.predict(x).reshape([height, width, 1])
result[result > 0.5] = 255
result[result <= 0.5] = 0
cv2.imwrite("data/result/{}.png".format(its[i]),
result.astype(np.uint8))
print("finish w {}".format(i))
X_test = np.array(salience_maps)
X_test = np.concatenate(X_test, axis=0)
Y_test = np.array(ground_truths)
Y_test = np.concatenate(Y_test, axis=0)
mlp.test(X_test, Y_test)
df = pd.DataFrame(rf_sal_weight / len(img_datas))
df.to_csv("data/csv/rf_sal_weight.csv")
