def benchmark(weights_filename, r, verbose = "True"):
filepath = 'model/weights/' + weights_filename # filepath of weights
num_images = 729 # number of testing images to benchmark on (<=729)
if verbose == "True":
verbose = True
else:
verbose = False
# Compile model
opt = tf.keras.optimizers.Adam(learning_rate=0.001)
# Peak Signal-to-Noise Ratio
def PSNR(y_true, y_pred):
max_pixel = 1.0
return tf.image.psnr(y_true, y_pred, max_val=max_pixel)
model = espcn(r)
model.compile(optimizer=opt, loss='mse', metrics=[PSNR])
# Initialize testing generator
testing_generator = DataGenerator('LRbicx' + str(r), batch_size = 1, dictionary = "test")
# Load weights
model.load_weights(filepath)
# Calculate average PSNR of all testing data
average_psnr = 0
for i in range(0, num_images):
lr, hr = testing_generator.__getitem__(i)
sr = model.predict(lr)
result = psnr(sr[0], hr[0])
average_psnr += result
if verbose:
print('Image: ' + str(i) + ', PSNR: ' + str(result) + ', Average: ' + str(average_psnr/(i+1)))
print("Average PSNR: " + str(average_psnr/num_images))
def train(conf):
gan = KernelGAN(conf)
learner = Learner()
data = DataGenerator(conf, gan)
for iteration in tqdm.tqdm(range(conf.max_iters), ncols=60):
[g_in, d_in] = data.__getitem__(iteration)
gan.train(g_in, d_in)
learner.update(iteration, gan)
gan.finish()
def estimate_kernel(img_file):
conf = config_kernelGAN(img_file)
kgan = KernelGAN(conf)
learner = Learner()
data = DataGenerator(conf, kgan)
for iteration in tqdm.tqdm(range(conf.max_iters), ncols=70):
[g_in, d_in, _] = data.__getitem__(iteration)
kgan.train(g_in, d_in)
learner.update(iteration, kgan)
kgan.finish()
def train(conf):
sr_net = DBPISR(conf)
learner = Learner()
data = DataGenerator(conf, sr_net)
for iteration in tqdm.tqdm(range(conf.max_iters), ncols=60):
g_in = data.__getitem__(iteration)
sr_net.train(g_in)
learner.update(iteration, sr_net)
sr_net.finish(data.input_image)
