from mr.learn.supervised.perceptron import Perceptron folder = '/stash/tlab/datasets/Tower' file_pre = 'Neovision2-Training-Tower-' w_new = 20 tau = 1 train_folders = ['1', '2', '3', '4', '5'] test_folders = ['13', '14'] """pickle_data = (model, model2, vp, xP, y)""" pickle_file = open('model_28.p', 'rb') pickle_data = pickle.load(pickle_file) pickle_file.close() model, model2, vp, xP, y = pickle_data model2 = TowerScaffold() videos_to_train = [os.path.join(folder, 'dev_test', 'train2', 'ims')] videos_to_test = [os.path.join(folder, 'dev_test', 'test2', 'ims')] train_csv = [os.path.join(folder, 'dev_test', 'train2', 'train.csv')] test_csv = [os.path.join(folder, 'dev_test', 'test2', 'train.csv')] t = st(vp[0], videos_to_train, videos_to_test, train_csv, test_csv) dices = model2._get_Dices(xP, y, 5) fig = model2.plot_Dices(dices, t.classes) plt.show()
model.fit(*train) ''' path = 'visualize.png' path2 = 'visualize2.png' model.visualize(vp, path) model.visualize(vp, path2, inputs = test[0][0]) ''' stop = datetime.datetime.now() train_min = (stop - start).total_seconds() / 60 print ("Total min to train: {}".format(train_min)) print ("Testing model") start = datetime.datetime.now() model2 = TowerScaffold() xP = model.predict(test[0], False) xP[xP > 0.5] = 1 xP[xP <= 0.5] = 0 y = np.asarray(test[1]) dice = model2._get_Dices(xP, y, len(t.classes)) print (dice) print ("Average Dice Coefficient = {}".format(np.mean(dice))) stop = datetime.datetime.now() test_min = (stop - start).total_seconds() / 60 print ("Total min to test: {}".format(test_min)) print ("Pickling...") pickle_file = open("/u/jc2/dev/jc2/cnn/model_{}.p".format(w_new), 'wb') pickle_data = (model, model2, vp, xP, y, dice) pickle.dump(pickle_data, pickle_file)
print (model.layers[0].nOutputsConvolved) ''' path = 'visualize.png' path2 = 'visualize2.png' model.visualize(vp, path) model.visualize(vp, path2, inputs = test[0][0]) ''' stop = datetime.datetime.now() train_min = (stop - start).total_seconds() / 60 print ("Total min to train: {}".format(train_min)) print ("Testing model") start = datetime.datetime.now() model2 = TowerScaffold() xP = model.predict(test[0], False) xP[xP > 0.5] = 1 xP[xP <= 0.5] = 0 y = np.asarray(test[1]) dice = model2._calc_Dice(xP, y) print (dice) print ("Average Dice Coefficient = {}".format(np.mean(dice))) stop = datetime.datetime.now() test_min = (stop - start).total_seconds() / 60 print ("Total min to test: {}".format(test_min)) pickle_file = open("/u/jc2/dev/jc2/cnn/model_{}.p".format(w_new), 'wb') pickle_data = (model, model2, vp, xP, y) pickle.dump(pickle_data, pickle_file)
from mr.learn.unsupervised.lca import Lca from mr.learn.supervised.perceptron import Perceptron import matplotlib.image as img import matplotlib.pyplot as plt from PIL import Image import datetime import pickle ## pickle_data = (t, test, train, vp, model, model2, xP, y) res = [4, 14, 28, 35, 50, 70] #res = [28, 35, 50, 70] res_names = [str(i) for i in res] t2 = TowerScaffold() dices = [] classes = OrderedDict([('car', 1), ('truck', 2), ('bus', 3), ('person', 4), ('cyclist', 5)]) ## Get data for i in res: pickle_file = open("/u/jc2/dev/jc2/cnn/model_{}.p".format(i), 'rb') _, _, _, _, _, _, xP, y = pickle.load(pickle_file) pickle_file.close() dices.append(t2._get_Dices(xP, y, 5)) ius = [np.divide(i, np.subtract(2, i)) for i in dices] #fig = t2.plot_Dice_res(dices, res, classes) fig = t2.plot_Dice_res(ius, res, classes)