File size: 7,554 Bytes
fcd5579 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
import numpy as np
from core.leras import nn
tf = nn.tf
patch_discriminator_kernels = \
{ 1 : (512, [ [1,1] ]),
2 : (512, [ [2,1] ]),
3 : (512, [ [2,1], [2,1] ]),
4 : (512, [ [2,2], [2,2] ]),
5 : (512, [ [3,2], [2,2] ]),
6 : (512, [ [4,2], [2,2] ]),
7 : (512, [ [3,2], [3,2] ]),
8 : (512, [ [4,2], [3,2] ]),
9 : (512, [ [3,2], [4,2] ]),
10 : (512, [ [4,2], [4,2] ]),
11 : (512, [ [3,2], [3,2], [2,1] ]),
12 : (512, [ [4,2], [3,2], [2,1] ]),
13 : (512, [ [3,2], [4,2], [2,1] ]),
14 : (512, [ [4,2], [4,2], [2,1] ]),
15 : (512, [ [3,2], [3,2], [3,1] ]),
16 : (512, [ [4,2], [3,2], [3,1] ]),
17 : (512, [ [3,2], [4,2], [3,1] ]),
18 : (512, [ [4,2], [4,2], [3,1] ]),
19 : (512, [ [3,2], [3,2], [4,1] ]),
20 : (512, [ [4,2], [3,2], [4,1] ]),
21 : (512, [ [3,2], [4,2], [4,1] ]),
22 : (512, [ [4,2], [4,2], [4,1] ]),
23 : (256, [ [3,2], [3,2], [3,2], [2,1] ]),
24 : (256, [ [4,2], [3,2], [3,2], [2,1] ]),
25 : (256, [ [3,2], [4,2], [3,2], [2,1] ]),
26 : (256, [ [4,2], [4,2], [3,2], [2,1] ]),
27 : (256, [ [3,2], [4,2], [4,2], [2,1] ]),
28 : (256, [ [4,2], [3,2], [4,2], [2,1] ]),
29 : (256, [ [3,2], [4,2], [4,2], [2,1] ]),
30 : (256, [ [4,2], [4,2], [4,2], [2,1] ]),
31 : (256, [ [3,2], [3,2], [3,2], [3,1] ]),
32 : (256, [ [4,2], [3,2], [3,2], [3,1] ]),
33 : (256, [ [3,2], [4,2], [3,2], [3,1] ]),
34 : (256, [ [4,2], [4,2], [3,2], [3,1] ]),
35 : (256, [ [3,2], [4,2], [4,2], [3,1] ]),
36 : (256, [ [4,2], [3,2], [4,2], [3,1] ]),
37 : (256, [ [3,2], [4,2], [4,2], [3,1] ]),
38 : (256, [ [4,2], [4,2], [4,2], [3,1] ]),
39 : (256, [ [3,2], [3,2], [3,2], [4,1] ]),
40 : (256, [ [4,2], [3,2], [3,2], [4,1] ]),
41 : (256, [ [3,2], [4,2], [3,2], [4,1] ]),
42 : (256, [ [4,2], [4,2], [3,2], [4,1] ]),
43 : (256, [ [3,2], [4,2], [4,2], [4,1] ]),
44 : (256, [ [4,2], [3,2], [4,2], [4,1] ]),
45 : (256, [ [3,2], [4,2], [4,2], [4,1] ]),
46 : (256, [ [4,2], [4,2], [4,2], [4,1] ]),
}
class PatchDiscriminator(nn.ModelBase):
def on_build(self, patch_size, in_ch, base_ch=None, conv_kernel_initializer=None):
suggested_base_ch, kernels_strides = patch_discriminator_kernels[patch_size]
if base_ch is None:
base_ch = suggested_base_ch
prev_ch = in_ch
self.convs = []
for i, (kernel_size, strides) in enumerate(kernels_strides):
cur_ch = base_ch * min( (2**i), 8 )
self.convs.append ( nn.Conv2D( prev_ch, cur_ch, kernel_size=kernel_size, strides=strides, padding='SAME', kernel_initializer=conv_kernel_initializer) )
prev_ch = cur_ch
self.out_conv = nn.Conv2D( prev_ch, 1, kernel_size=1, padding='VALID', kernel_initializer=conv_kernel_initializer)
def forward(self, x):
for conv in self.convs:
x = tf.nn.leaky_relu( conv(x), 0.1 )
return self.out_conv(x)
nn.PatchDiscriminator = PatchDiscriminator
class UNetPatchDiscriminator(nn.ModelBase):
"""
Inspired by https://arxiv.org/abs/2002.12655 "A U-Net Based Discriminator for Generative Adversarial Networks"
"""
def calc_receptive_field_size(self, layers):
"""
result the same as https://fomoro.com/research/article/receptive-field-calculatorindex.html
"""
rf = 0
ts = 1
for i, (k, s) in enumerate(layers):
if i == 0:
rf = k
else:
rf += (k-1)*ts
ts *= s
return rf
def find_archi(self, target_patch_size, max_layers=9):
"""
Find the best configuration of layers using only 3x3 convs for target patch size
"""
s = {}
for layers_count in range(1,max_layers+1):
val = 1 << (layers_count-1)
while True:
val -= 1
layers = []
sum_st = 0
layers.append ( [3, 2])
sum_st += 2
for i in range(layers_count-1):
st = 1 + (1 if val & (1 << i) !=0 else 0 )
layers.append ( [3, st ])
sum_st += st
rf = self.calc_receptive_field_size(layers)
s_rf = s.get(rf, None)
if s_rf is None:
s[rf] = (layers_count, sum_st, layers)
else:
if layers_count < s_rf[0] or \
( layers_count == s_rf[0] and sum_st > s_rf[1] ):
s[rf] = (layers_count, sum_st, layers)
if val == 0:
break
x = sorted(list(s.keys()))
q=x[np.abs(np.array(x)-target_patch_size).argmin()]
return s[q][2]
def on_build(self, patch_size, in_ch, base_ch = 16, use_fp16 = False):
self.use_fp16 = use_fp16
conv_dtype = tf.float16 if use_fp16 else tf.float32
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, kernel_size=3 ):
self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
def forward(self, inp):
x = self.conv1(inp)
x = tf.nn.leaky_relu(x, 0.2)
x = self.conv2(x)
x = tf.nn.leaky_relu(inp + x, 0.2)
return x
prev_ch = in_ch
self.convs = []
self.upconvs = []
layers = self.find_archi(patch_size)
level_chs = { i-1:v for i,v in enumerate([ min( base_ch * (2**i), 512 ) for i in range(len(layers)+1)]) }
self.in_conv = nn.Conv2D( in_ch, level_chs[-1], kernel_size=1, padding='VALID', dtype=conv_dtype)
for i, (kernel_size, strides) in enumerate(layers):
self.convs.append ( nn.Conv2D( level_chs[i-1], level_chs[i], kernel_size=kernel_size, strides=strides, padding='SAME', dtype=conv_dtype) )
self.upconvs.insert (0, nn.Conv2DTranspose( level_chs[i]*(2 if i != len(layers)-1 else 1), level_chs[i-1], kernel_size=kernel_size, strides=strides, padding='SAME', dtype=conv_dtype) )
self.out_conv = nn.Conv2D( level_chs[-1]*2, 1, kernel_size=1, padding='VALID', dtype=conv_dtype)
self.center_out = nn.Conv2D( level_chs[len(layers)-1], 1, kernel_size=1, padding='VALID', dtype=conv_dtype)
self.center_conv = nn.Conv2D( level_chs[len(layers)-1], level_chs[len(layers)-1], kernel_size=1, padding='VALID', dtype=conv_dtype)
def forward(self, x):
if self.use_fp16:
x = tf.cast(x, tf.float16)
x = tf.nn.leaky_relu( self.in_conv(x), 0.2 )
encs = []
for conv in self.convs:
encs.insert(0, x)
x = tf.nn.leaky_relu( conv(x), 0.2 )
center_out, x = self.center_out(x), tf.nn.leaky_relu( self.center_conv(x), 0.2 )
for i, (upconv, enc) in enumerate(zip(self.upconvs, encs)):
x = tf.nn.leaky_relu( upconv(x), 0.2 )
x = tf.concat( [enc, x], axis=nn.conv2d_ch_axis)
x = self.out_conv(x)
if self.use_fp16:
center_out = tf.cast(center_out, tf.float32)
x = tf.cast(x, tf.float32)
return center_out, x
nn.UNetPatchDiscriminator = UNetPatchDiscriminator
|