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evaluate_enhancement.py
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evaluate_enhancement.py
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from __future__ import print_function
from ssim import MultiScaleSSIM
from models import srcnn
import tensorflow as tf
from scipy import misc
import numpy as np
import utils
import os
## --------- Change test parameters below -----------
from models import srcnn as test_model # import your model definition as "test_model"
model_location = "models_pretrained/dped_srcnn" # specify the location of your saved pre-trained model (ckpt file)
compute_PSNR_SSIM = True
compute_running_time = True
if __name__ == "__main__":
print("\n-------------------------------------\n")
print("Image Super-resolution task\n")
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
np.warnings.filterwarnings('ignore')
###############################################################
# 1 Produce .pb model file that will be used for validation #
###############################################################
print("Saving pre-trained model as .pb file")
g = tf.Graph()
with g.as_default(), tf.Session() as sess:
image_ = tf.placeholder(tf.float32, shape=(1, None, None, 3), name="input")
out_ = tf.identity(test_model(image_), name="output")
saver = tf.train.Saver()
saver.restore(sess, model_location)
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, g.as_graph_def(), "input,output".split(",")
)
tf.train.write_graph(output_graph_def, 'models_converted', 'model.pb', as_text=False)
print("Model was successfully saved!")
print("\n-------------------------------------\n")
sess.close()
if compute_PSNR_SSIM:
#######################################
# 2 Computing PSNR / MS-SSIM scores #
#######################################
tf.reset_default_graph()
config = None
with tf.Session(config=config) as sess:
print("\rLoading pre-trained model")
with tf.gfile.FastGFile("models_converted/model.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
x_ = sess.graph.get_tensor_by_name('input:0')
out_ = sess.graph.get_tensor_by_name('output:0')
y_ = tf.placeholder(tf.float32, [1, None, None, 3])
output_crop_ = tf.clip_by_value(out_, 0.0, 1.0)
target_crop_ = tf.clip_by_value(y_, 0.0, 1.0)
psnr_ = tf.image.psnr(output_crop_, target_crop_, max_val=1.0)
print("Computing PSNR/SSIM scores....")
ssim_score = 0.0
psnr_score = 0.0
validation_images = os.listdir("dped/patches/canon/")
num_val_images = len(validation_images)
for j in range(num_val_images):
image_phone = misc.imread("dped/patches/iphone/" + validation_images[j])
image_dslr = misc.imread("dped/patches/canon/" + validation_images[j])
image_phone = np.float32(np.reshape(image_phone, [1, image_phone.shape[0], image_phone.shape[1], 3])) / 255
image_dslr = np.float32(np.reshape(image_dslr, [1, image_dslr.shape[0], image_dslr.shape[1], 3])) / 255
[psnr, enhanced] = sess.run([psnr_, out_], feed_dict={x_: image_phone, y_: image_dslr})
psnr_score += psnr / num_val_images
ssim_score += MultiScaleSSIM(image_dslr * 255, enhanced * 255) / num_val_images
print("\r\r\r")
print("Scores | PSNR: %.4g, MS-SSIM: %.4g" % (psnr_score, ssim_score))
print("\n-------------------------------------\n")
sess.close()
if compute_running_time:
##############################
# 3 Computing running time #
##############################
print("Evaluating model speed")
print("This can take a few minutes\n")
tf.reset_default_graph()
print("Testing pre-trained baseline SRCNN model")
avg_time_baseline, max_ram = utils.compute_running_time("superres", "models_pretrained/dped_srcnn.pb", "dped/HD_res/")
tf.reset_default_graph()
print("Testing provided model")
avg_time_solution, max_ram = utils.compute_running_time("superres", "models_converted/model.pb", "dped/HD_res/")
print("Baseline SRCNN time, ms: ", avg_time_baseline)
print("Test model time, ms: ", avg_time_solution)
print("Speedup ratio (baseline, ms / solution, ms): %.4f" % (float(avg_time_baseline) / avg_time_solution))
print("Approximate RAM consumption (HD image): " + str(max_ram) + " MB")