def update_lambda_every_meta(ori, up, hyper_lr, updatemode):
tmp = []
for x, y in zip(ori, up):
if updatemode == 'unit':
new_y = np.mean(y, axis=1, keepdims=True)
tmp.append(x.get_value() - np.sign(new_y) * np.array(float(hyper_lr) * 1., dtype=theano.config.floatX))
print("metaupdate", x.get_value()[0][1], tmp[-1][0][1])
else:
raise AttributeError
return tmp
if __name__ == '__main__':
args = setup()
print('all argument: ', args)
temp_lambda = None
loss_change = []
tmp_weights = None
for i in range(args.metaEpoch):
# Michael: update_lambda = temp_lambda, fix_weight = tmp_weights in run_exp
origin_lambda, temp_lambda, tmp_weights, eval_loss, eval_err = run_exp(args, temp_lambda, tmp_weights)
temp_lambda = update_lambda_every_meta(origin_lambda, temp_lambda, args.lrHyper, 'unit')
loss_change.append((float(eval_loss), eval_err))
print("---------------------------------------------------------------------------------------------------")
print("Training Result: ")
for k, v in enumerate(loss_change):
print(k, v)
print("---------------------------------------------------------------------------------------------------")
import cv2
import os
import args
import imageio
import numpy
from PIL import Image
arguments = args.setup()
videoSource = arguments.video
requestedResolution = int(arguments.resolution)
if requestedResolution < 1 or requestedResolution > 3:
raise Exception('Requested resolution is not in the acceptable range')
videoDestinationPath = arguments.out
if __name__ == '__main__':
if videoSource == '':
raise Exception('requested frame is longer than video.')
if not os.path.isfile(videoSource):
raise Exception('video not found.')
video = cv2.VideoCapture(videoSource)
videoLength = int(video.get(cv2.CAP_PROP_FRAME_COUNT)
) #returns the total number of frames in video
videoFrameWidth = video.get(
cv2.CAP_PROP_FRAME_WIDTH) #returns the width size of frames in video
videoFrameHeight = video.get(
cv2.CAP_PROP_FRAME_HEIGHT) #returns the height size of frames in video