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)
