AirSimNetTrainingWithDrone/Testing_loading_model.py at master · nectostr/AirSimNetTrainingWithDrone · GitHub

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# net import
import numpy as np
import keras.backend as K
import tensorflow as tf
import keras
from keras.regularizers import *
from keras.constraints import *
from keras.models import Sequential
from keras.layers import *
from keras.utils import np_utils
from keras.datasets import mnist
from matplotlib import pyplot as plt
np.random.seed(123)

config = tf.ConfigProto(
        device_count = {'GPU': 0}
    )
sess = tf.Session(config=config)
K.set_session(sess)
ROWS = 64
COLS = 64


def show_images(images, cols=1, titles=None):
    """Display a list of images in a single figure with matplotlib.

    Parameters
    —-------
    images: List of np.arrays compatible with plt.imshow.

    cols (Default = 1): Number of columns in figure (number of rows is
                        set to np.ceil(n_images/float(cols))).

    titles: List of titles corresponding to each image. Must have
            the same length as titles.
    """
    assert ((titles is None) or (len(images) == len(titles)))
    n_images = len(images)
    if titles is None: titles = ['Image (%d)' % i for i in range(1, n_images + 1)]
    fig = plt.figure()
    for n, (image, title) in enumerate(zip(images, titles)):
        a = fig.add_subplot(cols, np.ceil(n_images / float(cols)), n + 1)
        if image.ndim == 2:
            plt.gray()
        plt.imshow(image)
        a.set_title(title)
    fig.set_size_inches(np.array(fig.get_size_inches()) * n_images)
    plt.show()

path = 'L:\\Documents\\PyCharmProjects\\HelloDrone\\data10'
size = 560
def generator():
    i = np.random.randint(1, size)
    while True:
        x = np.loadtxt(path + "\\pic_from" + str(i) + ".txt")
        y = np.loadtxt(path + "\\pic_to" + str(i) + ".txt")
        x = np.expand_dims(np.expand_dims(np.expand_dims(x, 0),-1),0)
        y = np.expand_dims(np.expand_dims(y, 0), -1)
        if i == size: i = 1
        i += 1
        yield x,y

model = keras.models.load_model('model5.h5')
#a = generator()
#for i in range(5):
#    x_data, y_data = next(a)
#    res = model.predict(x_data)
#    np.savetxt("L:\\Documents\\PyCharmProjects\\HelloDrone\\to_check_data\\pic" + str(i) + ".txt", res)


print(model.summary())
epochs = 1
ep = 0
a = generator()
while ep < 10000:

    try:
        print(ep)
        history = model.fit_generator(a, epochs=epochs, steps_per_epoch=5, verbose=1, workers=1)
        x_data, y_data = next(a)
        res = model.predict(x_data)
        # show_images([np.reshape(x_data, (ROWS, COLS)), np.reshape(y_data, (ROWS, COLS)), np.reshape(res,(ROWS, COLS)),
        #            ], 1, ["from", "want", "predict"])
        #airsimdata.resetImageConn()
        if history.history['loss'] == np.nan:
            break
        model.reset_states()
        #if ep % 5 == 0:
            #show_images([np.reshape(x_data, (ROWS, COLS)), np.reshape(y_data, (ROWS, COLS)), np.reshape(res, (ROWS, COLS)),
            #            ], 1, ["from", "want", "predict"])
            #model.save('model5' + str(ep % 5) +'.h5')
        if ep % 2000 == 0:
            model.save('model5'+str(ep)+'.h5')
    except airsimdata.ExeptInGenData as ex:
        model.reset_states()
    finally:
        ep += 1
print(history.history['loss'])
for i in range(10):
    for j in range(10):
        x_data, y_data = next(generator())
        res = model.predict(x_data)
    # show_images([np.reshape(x_data, (ROWS, COLS)), np.reshape(y_data, (ROWS, COLS)), np.reshape(res,(ROWS, COLS)),
    #             ], 1, ["from", "want", "predict"])


print("<3")