Repository: Saafke/EDSR_Tensorflow
Branch: master
Commit: 06c7bd65b030
Files: 10
Total size: 110.3 MB
Directory structure:
gitextract_1ya4zebo/
├── LICENSE
├── README.md
├── data_utils.py
├── edsr.py
├── main.py
├── models/
│ ├── EDSR_x2.pb
│ ├── EDSR_x3.pb
│ └── EDSR_x4.pb
├── requirements.txt
└── run.py
================================================
FILE CONTENTS
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================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# EDSR in Tensorflow
TensorFlow implementation of [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/pdf/1707.02921.pdf)[1].
It was trained on the [Div2K dataset](https://data.vision.ee.ethz.ch/cvl/DIV2K/) - Train Data (HR images).
## Google Summer of Code with OpenCV
This repository was made during the 2019 GSoC program for the organization OpenCV. The [trained models (.pb files)](https://github.com/Saafke/EDSR_Tensorflow/tree/master/models/) can easily be used for inference in OpenCV with the ['dnn_superres' module](https://github.com/opencv/opencv_contrib/tree/master/modules/dnn_superres). See the OpenCV documentation for how to do this.
## Requirements
- tensorflow
- numpy
- cv2
## EDSR
This is the EDSR model, which has a different model for each scale. Architecture shown below. Go to branch 'mdsr' for the MDSR model.
# Running
Download [Div2K dataset](https://data.vision.ee.ethz.ch/cvl/DIV2K/). If you want to use another dataset, you will have to calculate the mean of that dataset, and set the new mean in 'main.py'. Code for calculating the mean can be found in data_utils.py.
Train:
- from scratch
`python main.py --train --fromscratch --scale <scale> --traindir /path-to-train-images/`
- resume/load previous
`python main.py --train --scale <scale> --traindir /path-to-train-images/`
Test (compares edsr with bicubic with PSNR metric):
`python main.py --test --scale <scale> --image /path-to-image/`
Upscale (with edsr):
`python main.py --upscale --scale <scale> --image /path-to-image/`
Export to .pb
`python main.py --export --scale <scale>`
Extra arguments (Nr of resblocks, filters, batch, lr etc.)
`python main.py --help`
## Example
(1) Original picture\
(2) Input image\
(3) Bicubic scaled (3x) image\
(4) EDSR scaled (3x) image\
## Notes
The .pb files in these repository are quantized. This is done purely to shrink the filesizes down from ~150MB to ~40MB, because GitHub does not allow uploads above 100MB. The performance loss due to quantization is minimal. (To quantize during exporting use $ --quant <1,2 or 3> (2 is recommended.))
## References
[1] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee, **"Enhanced Deep Residual Networks for Single Image Super-Resolution,"** <i>2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution in conjunction with **CVPR 2017**. </i> [[PDF](http://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Lim_Enhanced_Deep_Residual_CVPR_2017_paper.pdf)] [[arXiv](https://arxiv.org/abs/1707.02921)] [[Slide](https://cv.snu.ac.kr/research/EDSR/Presentation_v3(release).pptx)]
================================================
FILE: data_utils.py
================================================
import pathlib
import os
from PIL import Image
import numpy as np
import cv2
import tensorflow as tf
import random
def getpathsx(path):
"""
Get all image paths from folder 'path'.
"""
data = pathlib.Path(path)
all_image_paths = list(data.glob('*'))
all_image_paths = [str(p) for p in all_image_paths]
return all_image_paths
def getpaths(path):
"""
Get all image paths from folder 'path' while avoiding ._ files.
"""
im_paths = []
for fil in os.listdir(path):
if '.png' in fil:
if "._" in fil:
#avoid dot underscore
pass
else:
im_paths.append(os.path.join(path, fil))
return im_paths
def make_val_dataset(paths, scale, mean):
"""
Python generator-style dataset for the validation set. Creates input and ground truth.
"""
for p in paths:
# normalize
im_norm = cv2.imread(p.decode(), 3).astype(np.float32) - mean
# divisible by scale - create low-res
hr = im_norm[0:(im_norm.shape[0] - (im_norm.shape[0] % scale)),
0:(im_norm.shape[1] - (im_norm.shape[1] % scale)), :]
lr = cv2.resize(hr, (int(hr.shape[1] / scale), int(hr.shape[0] / scale)),
interpolation=cv2.INTER_CUBIC)
yield lr, hr
def make_dataset(paths, scale, mean):
"""
Python generator-style dataset. Creates 48x48 low-res and corresponding high-res patches.
"""
size_lr = 48
size_hr = size_lr * scale
for p in paths:
# normalize
im_norm = cv2.imread(p.decode(), 3).astype(np.float32) - mean
# random flip
r = random.randint(-1, 2)
if not r == 2:
im_norm = cv2.flip(im_norm, r)
# divisible by scale - create low-res
hr = im_norm[0:(im_norm.shape[0] - (im_norm.shape[0] % scale)),
0:(im_norm.shape[1] - (im_norm.shape[1] % scale)), :]
lr = cv2.resize(hr, (int(hr.shape[1] / scale), int(hr.shape[0] / scale)),
interpolation=cv2.INTER_CUBIC)
numx = int(lr.shape[0] / size_lr)
numy = int(lr.shape[1] / size_lr)
for i in range(0, numx):
startx = i * size_lr
endx = (i * size_lr) + size_lr
startx_hr = i * size_hr
endx_hr = (i * size_hr) + size_hr
for j in range(0, numy):
starty = j * size_lr
endy = (j * size_lr) + size_lr
starty_hr = j * size_hr
endy_hr = (j * size_hr) + size_hr
crop_lr = lr[startx:endx, starty:endy]
crop_hr = hr[startx_hr:endx_hr, starty_hr:endy_hr]
x = crop_lr.reshape((size_lr, size_lr, 3))
y = crop_hr.reshape((size_hr, size_hr, 3))
yield x, y
def calcmean(imageFolder, bgr):
"""
Calculates the mean of a dataset.
"""
paths = getpaths(imageFolder)
total_mean = [0, 0, 0]
im_counter = 0
for p in paths:
image = np.asarray(Image.open(p))
mean_rgb = np.mean(image, axis=(0, 1), dtype=np.float64)
if im_counter % 50 == 0:
print("Total mean: {} | current mean: {}".format(total_mean, mean_rgb))
total_mean += mean_rgb
im_counter += 1
total_mean /= im_counter
# rgb to bgr
if bgr is True:
total_mean = total_mean[...,::-1]
return total_mean
================================================
FILE: edsr.py
================================================
from __future__ import print_function
import cv2
import tensorflow as tf
import numpy as np
import os
class Edsr:
def __init__(self, B, F, scale):
self.B = B
self.F = F
self.scale = scale
self.global_step = tf.placeholder(tf.int32, shape=[], name="global_step")
self.scaling_factor = 0.1
self.bias_initializer = tf.constant_initializer(value=0.0)
self.PS = 3 * (scale*scale) #channels x scale^2
self.xavier = tf.contrib.layers.xavier_initializer()
# -- Filters & Biases --
self.resFilters = list()
self.resBiases = list()
for i in range(0, B*2):
self.resFilters.append( tf.get_variable("resFilter%d" % (i), shape=[3,3,F,F], initializer=self.xavier))
self.resBiases.append(tf.get_variable(name="resBias%d" % (i), shape=[F], initializer=self.bias_initializer))
self.filter_one = tf.get_variable("resFilter_one", shape=[3,3,3,F], initializer=self.xavier)
self.filter_two = tf.get_variable("resFilter_two", shape=[3,3,F,F], initializer=self.xavier)
self.filter_three = tf.get_variable("resFilter_three", shape=[3,3,F,self.PS], initializer=self.xavier)
self.bias_one = tf.get_variable(shape=[F], initializer=self.bias_initializer, name="BiasOne")
self.bias_two = tf.get_variable(shape=[F], initializer=self.bias_initializer, name="BiasTwo")
self.bias_three = tf.get_variable(shape=[self.PS], initializer=self.bias_initializer, name="BiasThree")
def model(self, x, y, lr):
"""
Implementation of EDSR: https://arxiv.org/abs/1707.02921.
"""
# -- Model architecture --
# first conv
x = tf.nn.conv2d(x, filter=self.filter_one, strides=[1, 1, 1, 1], padding='SAME')
x = x + self.bias_one
out1 = tf.identity(x)
# all residual blocks
for i in range(self.B):
x = self.resBlock(x, (i*2))
# last conv
x = tf.nn.conv2d(x, filter=self.filter_two, strides=[1, 1, 1, 1], padding='SAME')
x = x + self.bias_two
x = x + out1
# upsample via sub-pixel, equivalent to depth to space
x = tf.nn.conv2d(x, filter=self.filter_three, strides=[1, 1, 1, 1], padding='SAME')
x = x + self.bias_three
out = tf.nn.depth_to_space(x, self.scale, data_format='NHWC', name="NHWC_output")
# -- --
# some outputs
out_nchw = tf.transpose(out, [0, 3, 1, 2], name="NCHW_output")
psnr = tf.image.psnr(out, y, max_val=255.0)
loss = tf.losses.absolute_difference(out, y) #L1
ssim = tf.image.ssim(out, y, max_val=255.0)
# (decaying) learning rate
lr = tf.train.exponential_decay(lr,
self.global_step,
decay_steps=15000,
decay_rate=0.95,
staircase=True)
# gradient clipping
optimizer = tf.train.AdamOptimizer(lr)
gradients, variables = zip(*optimizer.compute_gradients(loss))
gradients, _ = tf.clip_by_global_norm(gradients, 5.0)
train_op = optimizer.apply_gradients(zip(gradients, variables))
return out, loss, train_op, psnr, ssim, lr
def resBlock(self, inpt, f_nr):
x = tf.nn.conv2d(inpt, filter=self.resFilters[f_nr], strides=[1, 1, 1, 1], padding='SAME')
x = x + self.resBiases[f_nr]
x = tf.nn.relu(x)
x = tf.nn.conv2d(x, filter=self.resFilters[f_nr+1], strides=[1, 1, 1, 1], padding='SAME')
x = x + self.resBiases[f_nr+1]
x = x * self.scaling_factor
return inpt + x
================================================
FILE: main.py
================================================
import tensorflow as tf
import data_utils
import run
import os
import cv2
import numpy as np
import pathlib
import argparse
from PIL import Image
import numpy
from tensorflow.python.client import device_lib
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' #gets rid of avx/fma warning
# TODO:
# When starting training for x3 and x4, start from pre-trained x2 model.
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# bools
parser.add_argument('--train', help='Train the model', action="store_true")
parser.add_argument('--test', help='Run PSNR test on an image', action="store_true")
parser.add_argument('--upscale', help='Upscale an image with desired scale', action="store_true")
parser.add_argument('--export', help='Export the model as .pb', action="store_true")
parser.add_argument('--fromscratch', help='Load previous model for training',action="store_false")
# numbers
parser.add_argument('--quant', type=int, help='Quantize to shrink .pb file size. 1=round_weights. 2=quantize_weights. 3=round_weights&quantize.', default=0)
parser.add_argument('--B', type=int, help='Number of resBlocks', default=32)
parser.add_argument('--F', type=int, help='Number of filters', default=256)
parser.add_argument('--scale', type=int, help='Scaling factor of the model', default=2)
parser.add_argument('--batch', type=int, help='Batch size of the training', default=16)
parser.add_argument('--epochs', type=int, help='Number of epochs during training', default=20)
parser.add_argument('--lr', type=float, help='Learning_rate', default=0.0001)
# paths
parser.add_argument('--image', help='Specify test image', default="./images/original.png")
parser.add_argument('--traindir', help='Path to train images')
parser.add_argument('--validdir', help='Path to train images')
args = parser.parse_args()
# INIT
scale = args.scale
meanbgr = [103.1545782, 111.561547, 114.35629928]
# Set checkpoint paths for different scales and models
ckpt_path = ""
if scale == 2:
ckpt_path = "./CKPT_dir/x2/"
elif scale == 3:
ckpt_path = "./CKPT_dir/x3/"
elif scale == 4:
ckpt_path = "./CKPT_dir/x4/"
else:
print("No checkpoint directory. Choose scale 2, 3 or 4. Or add checkpoint directory for this scale.")
exit()
# Set gpu
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# Create run instance
run = run.run(config, ckpt_path, scale, args.batch, args.epochs, args.B, args.F, args.lr, args.fromscratch, meanbgr)
if args.train:
run.train(args.traindir, args.validdir)
if args.test:
run.testFromPb(args.image)
#run.test(args.image)
if args.upscale:
run.upscaleFromPb(args.image)
#run.upscale(args.image)
if args.export:
run.export(args.quant)
print("I ran successfully.")
================================================
FILE: models/EDSR_x2.pb
================================================
[File too large to display: 36.7 MB]
================================================
FILE: models/EDSR_x3.pb
================================================
[File too large to display: 36.7 MB]
================================================
FILE: models/EDSR_x4.pb
================================================
[File too large to display: 36.8 MB]
================================================
FILE: requirements.txt
================================================
numpy
opencv-python
tensorflow==1.14.0
Pillow
scikit-image
================================================
FILE: run.py
================================================
import tensorflow as tf
import os
import cv2
import numpy as np
import math
import data_utils
from skimage import io
import edsr
from PIL import Image
from tensorflow.python.tools import freeze_graph
from tensorflow.python.tools import optimize_for_inference_lib
from tensorflow.tools.graph_transforms import TransformGraph
class run:
def __init__(self, config, ckpt_path, scale, batch, epochs, B, F, lr, load_flag, meanBGR):
self.config = config
self.ckpt_path = ckpt_path
self.scale = scale
self.batch = batch
self.epochs = epochs
self.B = B
self.F = F
self.lr = lr
self.load_flag = load_flag
self.mean = meanBGR
def train(self, imagefolder, validfolder):
# Create training dataset
train_image_paths = data_utils.getpaths(imagefolder)
train_dataset = tf.data.Dataset.from_generator(generator=data_utils.make_dataset,
output_types=(tf.float32, tf.float32),
output_shapes=(tf.TensorShape([None, None, 3]), tf.TensorShape([None, None, 3])),
args=[train_image_paths, self.scale, self.mean])
train_dataset = train_dataset.padded_batch(self.batch, padded_shapes=([None, None, 3],[None, None, 3]))
# Create validation dataset
val_image_paths = data_utils.getpaths(validfolder)
val_dataset = tf.data.Dataset.from_generator(generator=data_utils.make_val_dataset,
output_types=(tf.float32, tf.float32),
output_shapes=(tf.TensorShape([None, None, 3]), tf.TensorShape([None, None, 3])),
args=[val_image_paths, self.scale, self.mean])
val_dataset = val_dataset.padded_batch(1, padded_shapes=([None, None, 3],[None, None, 3]))
# Make the iterator and its initializers
train_val_iterator = tf.data.Iterator.from_structure(train_dataset.output_types, train_dataset.output_shapes)
train_initializer = train_val_iterator.make_initializer(train_dataset)
val_initializer = train_val_iterator.make_initializer(val_dataset)
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(handle, train_dataset.output_types, train_dataset.output_shapes)
LR, HR = iterator.get_next()
# Edsr model
print("\nRunning EDSR.")
edsrObj = edsr.Edsr(self.B, self.F, self.scale)
out, loss, train_op, psnr, ssim, lr = edsrObj.model(x=LR, y=HR, lr=self.lr)
# -- Training session
with tf.Session(config=self.config) as sess:
train_writer = tf.summary.FileWriter('./logs/train', sess.graph)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
# Create check points directory if not existed, and load previous model if specified.
if not os.path.exists(self.ckpt_path):
os.makedirs(self.ckpt_path)
else:
if os.path.isfile(self.ckpt_path + "edsr_ckpt" + ".meta"):
if self.load_flag:
saver.restore(sess, tf.train.latest_checkpoint(self.ckpt_path))
print("\nLoaded checkpoint.")
if not self.load_flag:
print("No checkpoint loaded. Training from scratch.")
# else:
# if os.path.isfile("./CKPT_dir/x2/" + "edsr_ckpt" + ".meta"):
# saver.restore(sess, tf.train.latest_checkpoint("./CKPT_dir/x2/"))
# print("Previous checkpoint does not exists. Will load model from x2")
# else:
# print("No checkpoint loaded. Training from scratch.")
global_step = 0
tf.convert_to_tensor(global_step)
train_val_handle = sess.run(train_val_iterator.string_handle())
print("Training...")
for e in range(1, self.epochs+1):
sess.run(train_initializer)
step, train_loss = 0, 0
try:
while True:
o, l, t, l_rate = sess.run([out, loss, train_op, lr], feed_dict={handle:train_val_handle,
edsrObj.global_step: global_step})
train_loss += l
step += 1
global_step += 1
if step % 1000 == 0:
save_path = saver.save(sess, self.ckpt_path + "edsr_ckpt")
print("Step nr: [{}/{}] - Loss: {:.5f} - Lr: {:.7f}".format(step, "?", float(train_loss/step), l_rate))
except tf.errors.OutOfRangeError:
pass
# Perform end-of-epoch calculations here.
sess.run(val_initializer)
tot_val_psnr, tot_val_ssim, val_im_cntr = 0, 0, 0
try:
while True:
val_psnr, val_ssim = sess.run([psnr, ssim], feed_dict={handle:train_val_handle})
tot_val_psnr += val_psnr[0]
tot_val_ssim += val_ssim[0]
val_im_cntr += 1
except tf.errors.OutOfRangeError:
pass
print("Epoch nr: [{}/{}] - Loss: {:.5f} - val PSNR: {:.3f} - val SSIM: {:.3f}\n".format(e,
self.epochs,
float(train_loss/step),
(tot_val_psnr / val_im_cntr),
(tot_val_ssim / val_im_cntr)))
save_path = saver.save(sess, self.ckpt_path + "edsr_ckpt")
print("Training finished.")
train_writer.close()
def upscale(self, path):
"""
Upscales an image via model. This loads a checkpoint, not a .pb file.
"""
fullimg = cv2.imread(path, 3)
floatimg = fullimg.astype(np.float32) - self.mean
LR_input_ = floatimg.reshape(1, floatimg.shape[0], floatimg.shape[1], 3)
with tf.Session(config=self.config) as sess:
print("\nUpscale image by a factor of {}:\n".format(self.scale))
# load the model
ckpt_name = self.ckpt_path + "edsr_ckpt" + ".meta"
saver = tf.train.import_meta_graph(ckpt_name)
saver.restore(sess, tf.train.latest_checkpoint(self.ckpt_path))
graph_def = sess.graph
LR_tensor = graph_def.get_tensor_by_name("IteratorGetNext:0")
HR_tensor = graph_def.get_tensor_by_name("NHWC_output:0")
output = sess.run(HR_tensor, feed_dict={LR_tensor: LR_input_})
Y = output[0]
HR_image = (Y + self.mean).clip(min=0, max=255)
HR_image = (HR_image).astype(np.uint8)
bicubic_image = cv2.resize(fullimg, None, fx=self.scale, fy=self.scale, interpolation=cv2.INTER_CUBIC)
cv2.imshow('Original image', fullimg)
cv2.imshow('EDSR upscaled image', HR_image)
cv2.imshow('Bicubic upscaled image', bicubic_image)
cv2.waitKey(0)
sess.close()
def test(self, path):
"""
Test single image and calculate psnr. This loads a checkpoint, not a .pb file.
"""
fullimg = cv2.imread(path, 3)
width = fullimg.shape[0]
height = fullimg.shape[1]
cropped = fullimg[0:(width - (width % self.scale)), 0:(height - (height % self.scale)), :]
img = cv2.resize(cropped, None, fx=1. / self.scale, fy=1. / self.scale, interpolation=cv2.INTER_CUBIC)
floatimg = img.astype(np.float32) - self.mean
LR_input_ = floatimg.reshape(1, floatimg.shape[0], floatimg.shape[1], 3)
with tf.Session(config=self.config) as sess:
print("\nTest model with psnr:\n")
# load the model
ckpt_name = self.ckpt_path + "edsr_ckpt" + ".meta"
saver = tf.train.import_meta_graph(ckpt_name)
saver.restore(sess, tf.train.latest_checkpoint(self.ckpt_path))
graph_def = sess.graph
LR_tensor = graph_def.get_tensor_by_name("IteratorGetNext:0")
HR_tensor = graph_def.get_tensor_by_name("NHWC_output:0")
output = sess.run(HR_tensor, feed_dict={LR_tensor: LR_input_})
Y = output[0]
HR_image = (Y + self.mean).clip(min=0, max=255)
HR_image = (HR_image).astype(np.uint8)
bicubic_image = cv2.resize(img, None, fx=self.scale, fy=self.scale, interpolation=cv2.INTER_CUBIC)
print(np.amax(Y), np.amax(LR_input_))
print("PSNR of EDSR upscaled image: {}".format(self.psnr(cropped, HR_image)))
print("PSNR of bicubic upscaled image: {}".format(self.psnr(cropped, bicubic_image)))
cv2.imshow('Original image', fullimg)
cv2.imshow('EDSR upscaled image', HR_image)
cv2.imshow('Bicubic upscaled image', bicubic_image)
cv2.imwrite("./images/EdsrOutput.png", HR_image)
cv2.imwrite("./images/BicubicOutput.png", bicubic_image)
cv2.imwrite("./images/original.png", fullimg)
cv2.imwrite("./images/input.png", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
sess.close()
def load_pb(self, path_to_pb):
with tf.gfile.GFile(path_to_pb, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def, name='')
return graph
def testFromPb(self, path):
"""
Test single image and calculate psnr. This loads a .pb file.
"""
# Read model
pbPath = "./models/EDSR_x{}.pb".format(self.scale)
# Get graph
graph = self.load_pb(pbPath)
fullimg = cv2.imread(path, 3)
width = fullimg.shape[0]
height = fullimg.shape[1]
cropped = fullimg[0:(width - (width % self.scale)), 0:(height - (height % self.scale)), :]
img = cv2.resize(cropped, None, fx=1. / self.scale, fy=1. / self.scale, interpolation=cv2.INTER_CUBIC)
floatimg = img.astype(np.float32) - self.mean
LR_input_ = floatimg.reshape(1, floatimg.shape[0], floatimg.shape[1], 3)
LR_tensor = graph.get_tensor_by_name("IteratorGetNext:0")
HR_tensor = graph.get_tensor_by_name("NHWC_output:0")
with tf.Session(graph=graph) as sess:
print("Loading pb...")
output = sess.run(HR_tensor, feed_dict={LR_tensor: LR_input_})
Y = output[0]
HR_image = (Y + self.mean).clip(min=0, max=255)
HR_image = (HR_image).astype(np.uint8)
bicubic_image = cv2.resize(img, None, fx=self.scale, fy=self.scale, interpolation=cv2.INTER_CUBIC)
print(np.amax(Y), np.amax(LR_input_))
print("PSNR of EDSR upscaled image: {}".format(self.psnr(cropped, HR_image)))
print("PSNR of bicubic upscaled image: {}".format(self.psnr(cropped, bicubic_image)))
cv2.imshow('Original image', fullimg)
cv2.imshow('EDSR upscaled image', HR_image)
cv2.imshow('Bicubic upscaled image', bicubic_image)
cv2.imwrite("./images/EdsrOutput.png", HR_image)
cv2.imwrite("./images/BicubicOutput.png", bicubic_image)
cv2.imwrite("./images/original.png", fullimg)
cv2.imwrite("./images/input.png", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
print("Done.")
sess.close()
def upscaleFromPb(self, path):
"""
Upscale single image by desired model. This loads a .pb file.
"""
# Read model
pbPath = "./models/EDSR_x{}.pb".format(self.scale)
# Get graph
graph = self.load_pb(pbPath)
fullimg = cv2.imread(path, 3)
floatimg = fullimg.astype(np.float32) - self.mean
LR_input_ = floatimg.reshape(1, floatimg.shape[0], floatimg.shape[1], 3)
LR_tensor = graph.get_tensor_by_name("IteratorGetNext:0")
HR_tensor = graph.get_tensor_by_name("NHWC_output:0")
with tf.Session(graph=graph) as sess:
print("Loading pb...")
output = sess.run(HR_tensor, feed_dict={LR_tensor: LR_input_})
Y = output[0]
HR_image = (Y + self.mean).clip(min=0, max=255)
HR_image = (HR_image).astype(np.uint8)
bicubic_image = cv2.resize(fullimg, None, fx=self.scale, fy=self.scale, interpolation=cv2.INTER_CUBIC)
cv2.imshow('Original image', fullimg)
cv2.imshow('EDSR upscaled image', HR_image)
cv2.imshow('Bicubic upscaled image', bicubic_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
sess.close()
def export(self, quant):
print("Exporting model...")
export_dir = "./models/"
if not os.path.exists(export_dir):
os.makedirs(export_dir)
export_file = "EDSRorig_x{}.pb".format(self.scale)
graph = tf.get_default_graph()
with graph.as_default():
with tf.Session(config=self.config) as sess:
### Restore checkpoint
ckpt_name = self.ckpt_path + "edsr_ckpt" + ".meta"
saver = tf.train.import_meta_graph(ckpt_name)
saver.restore(sess, tf.train.latest_checkpoint(self.ckpt_path))
# Return a serialized GraphDef representation of this graph
graph_def = sess.graph.as_graph_def()
# All variables to constants
graph_def = tf.graph_util.convert_variables_to_constants(sess, graph_def, ['NCHW_output'])
# Optimize for inference
graph_def = optimize_for_inference_lib.optimize_for_inference(graph_def, ["IteratorGetNext"],
["NCHW_output"], # ["NHWC_output"],
tf.float32.as_datatype_enum)
# Implement certain file shrinking transforms. 2 is recommended.
transforms = ["sort_by_execution_order"]
if quant == 1:
print("Rounding weights for export.")
transforms = ["sort_by_execution_order", "round_weights"]
export_file = "EDSR_x{}_q1.pb".format(self.scale)
if quant == 2:
print("Quantizing for export.")
transforms = ["sort_by_execution_order", "quantize_weights"]
export_file = "EDSR_x{}.pb".format(self.scale)
if quant == 3:
print("Round weights and quantizing for export.")
transforms = ["sort_by_execution_order", "round_weights", "quantize_weights"]
export_file = "EDSR_x{}_q3.pb".format(self.scale)
graph_def = TransformGraph(graph_def, ["IteratorGetNext"],
["NCHW_output"],
transforms)
print("Exported file = {}".format(export_dir+export_file))
with tf.gfile.GFile(export_dir + export_file, 'wb') as f:
f.write(graph_def.SerializeToString())
tf.train.write_graph(graph_def, ".", 'train.pbtxt')
sess.close()
def psnr(self, img1, img2):
mse = np.mean( (img1 - img2) ** 2 )
if mse == 0:
return 100
PIXEL_MAX = 255.0
return (20 * math.log10(PIXEL_MAX / math.sqrt(mse)))
gitextract_1ya4zebo/ ├── LICENSE ├── README.md ├── data_utils.py ├── edsr.py ├── main.py ├── models/ │ ├── EDSR_x2.pb │ ├── EDSR_x3.pb │ └── EDSR_x4.pb ├── requirements.txt └── run.py
SYMBOL INDEX (19 symbols across 3 files)
FILE: data_utils.py
function getpathsx (line 9) | def getpathsx(path):
function getpaths (line 18) | def getpaths(path):
function make_val_dataset (line 32) | def make_val_dataset(paths, scale, mean):
function make_dataset (line 48) | def make_dataset(paths, scale, mean):
function calcmean (line 94) | def calcmean(imageFolder, bgr):
FILE: edsr.py
class Edsr (line 8) | class Edsr:
method __init__ (line 10) | def __init__(self, B, F, scale):
method model (line 37) | def model(self, x, y, lr):
method resBlock (line 85) | def resBlock(self, inpt, f_nr):
FILE: run.py
class run (line 15) | class run:
method __init__ (line 16) | def __init__(self, config, ckpt_path, scale, batch, epochs, B, F, lr, ...
method train (line 28) | def train(self, imagefolder, validfolder):
method upscale (line 135) | def upscale(self, path):
method test (line 170) | def test(self, path):
method load_pb (line 220) | def load_pb(self, path_to_pb):
method testFromPb (line 228) | def testFromPb(self, path):
method upscaleFromPb (line 279) | def upscaleFromPb(self, path):
method export (line 314) | def export(self, quant):
method psnr (line 370) | def psnr(self, img1, img2):
Condensed preview — 10 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (42K chars).
[
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 2977,
"preview": "# EDSR in Tensorflow\n\nTensorFlow implementation of [Enhanced Deep Residual Networks for Single Image Super-Resolution](h"
},
{
"path": "data_utils.py",
"chars": 3473,
"preview": "import pathlib\nimport os\nfrom PIL import Image\nimport numpy as np\nimport cv2\nimport tensorflow as tf\nimport random\n\ndef "
},
{
"path": "edsr.py",
"chars": 3721,
"preview": "from __future__ import print_function\n\nimport cv2\nimport tensorflow as tf\nimport numpy as np\nimport os\n\nclass Edsr:\n\n "
},
{
"path": "main.py",
"chars": 2922,
"preview": "import tensorflow as tf\nimport data_utils\nimport run\nimport os\nimport cv2\nimport numpy as np\nimport pathlib\nimport argpa"
},
{
"path": "requirements.txt",
"chars": 59,
"preview": "numpy\nopencv-python\ntensorflow==1.14.0\nPillow\nscikit-image\n"
},
{
"path": "run.py",
"chars": 16350,
"preview": "import tensorflow as tf\nimport os\nimport cv2\nimport numpy as np\nimport math\nimport data_utils\nfrom skimage import io\nimp"
}
]
// ... and 3 more files (download for full content)
About this extraction
This page contains the full source code of the Saafke/EDSR_Tensorflow GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 10 files (110.3 MB), approximately 9.6k tokens, and a symbol index with 19 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.