Mirrors_Framework/FSRCNN-TensorFlow
1
0
Fork 0
mirror of https://github.com/igv/FSRCNN-TensorFlow.git synced 2025-12-16 01:24:28 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Almost ready

Browse Source
This commit is contained in:
Drake Levy
2017-03-01 17:59:31 -08:00
parent 906cfe03ff
commit 007e421178
1 changed files with 6 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
model.py
View File

@@ -17,9 +17,9 @@ import tensorflow as tf
from PIL import Image
import pdb
# Based on http://mmlab.ie.cuhk.edu.hk/projects/FSRCNN.html
class FSRCNN(object):
def __init__(self, sess, config):
self.sess = sess
self.fast = config.fast
@@ -36,12 +36,13 @@ class FSRCNN(object):
self.params = config.params
# Different image/label sub-sizes for different scaling factors x2, x3, x4
scale_factors = [[10, 20], [11, 21], [6, 24]]
scale_factors = [[14, 20], [11, 21], [10, 24]]
self.image_size, self.label_size = scale_factors[self.scale - 2]
# Testing uses different strides to ensure sub-images line up correctly
if not self.train:
self.stride = [10, 7, 6][self.scale - 2]
# Different model layer counts/filter sizes for FSRCNN vs FSRCNN-s (fast)
# Different model layer counts and filter sizes for FSRCNN vs FSRCNN-s (fast), (s, d, m) in paper
model_params = [[56, 12, 4], [32, 5, 1]]
self.model_params = model_params[self.fast]
@@ -57,7 +58,7 @@ class FSRCNN(object):
# Batch size differs in training vs testing
self.batch = tf.placeholder(tf.int32, shape=[], name='batch')
# FSCRNN-s (fast) has smaller filters and less layers but can achieve realtime performance
# FSCRNN-s (fast) has smaller filters and less layers but can achieve faster performance
s, d, m = self.model_params
expand_weight, deconv_weight = 'w{}'.format(m + 3), 'w{}'.format(m + 4)