def update_g(self, optimizer):
noisev = Variable(
np.asarray(np.random.normal(size=(self.args.batch_size, self.args.nz, 1, 1)), dtype=np.float32))
noisev.to_device(self.device)
fake = self.G(noisev)
errG = self.D(fake)
optimizer.update(self.g_loss, errG)
def _(bm):
y = Variable(numpy.random.rand(*shape))
t = Variable(numpy.random.rand(*shape))
y.to_device(0)
t.to_device(0)
with bm:
ssim_depthwise_convolution(y, t, 11, 1)
def _(bm):
y = Variable(numpy.random.rand(*shape))
t = Variable(numpy.random.rand(*shape))
y.to_device(0)
t.to_device(0)
with bm:
ssim_im2col(y, t, 11, 1)
def update_d(self, optimizer):
batch = self.get_iterator('main').next()
inputv = Variable(self.converter(batch, self.device))
errD_real = self.D(inputv)
# train with fake
noisev = Variable(
np.asarray(np.random.normal(size=(self.args.batch_size, self.args.nz, 1, 1)), dtype=np.float32))
noisev.to_device(self.device)
fake = self.G(noisev)
errD_fake = self.D(fake)
optimizer.update(self.d_loss, errD_real, errD_fake)
if __name__ == '__main__':
parser = argparse.ArgumentParser("Maximize SSIM")
parser.add_argument("--device", type=int, default=-1)
parser.add_argument("--noplot", dest="is_plot", action="store_false")
args = parser.parse_args()
device = chainer.get_device(args.device)
img1 = cv2.imread("assets/einstein.png")
img1 = img1.astype(np.float32).transpose(2, 0, 1) / 255.0
img1 = np.expand_dims(img1, 0)
img1 = Variable(img1)
img1.to_device(device)
img2 = L.Parameter(np.random.rand(*img1.shape).astype(np.float32))
img2.to_device(device)
optimizer = Adam(0.1)
optimizer.setup(img2)
device.use()
print(type(img1), type(img2()))
ssim_value = ssim_loss(img1, img2(), 11, 11)
print("Initial ssim:", ssim_value)
step = 1
while ssim_value.data < 0.95:
