forked from PaddlePaddle/models
-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathtrainer.py
162 lines (150 loc) · 7.26 KB
/
trainer.py
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
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from model import build_generator_resnet_9blocks, build_gen_discriminator
import paddle.fluid as fluid
step_per_epoch = 1335
cycle_loss_factor = 10.0
class GATrainer():
def __init__(self, input_A, input_B):
self.program = fluid.default_main_program().clone()
with fluid.program_guard(self.program):
self.fake_B = build_generator_resnet_9blocks(input_A, name="g_A")
self.fake_A = build_generator_resnet_9blocks(input_B, name="g_B")
self.cyc_A = build_generator_resnet_9blocks(self.fake_B, "g_B")
self.cyc_B = build_generator_resnet_9blocks(self.fake_A, "g_A")
self.infer_program = self.program.clone()
diff_A = fluid.layers.abs(
fluid.layers.elementwise_sub(
x=input_A, y=self.cyc_A))
diff_B = fluid.layers.abs(
fluid.layers.elementwise_sub(
x=input_B, y=self.cyc_B))
self.cyc_loss = (
fluid.layers.reduce_mean(diff_A) +
fluid.layers.reduce_mean(diff_B)) * cycle_loss_factor
self.fake_rec_B = build_gen_discriminator(self.fake_B, "d_B")
self.disc_loss_B = fluid.layers.reduce_mean(
fluid.layers.square(self.fake_rec_B - 1))
self.g_loss_A = fluid.layers.elementwise_add(self.cyc_loss,
self.disc_loss_B)
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and var.name.startswith("g_A"):
vars.append(var.name)
self.param = vars
lr = 0.0002
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.piecewise_decay(
boundaries=[
100 * step_per_epoch, 120 * step_per_epoch,
140 * step_per_epoch, 160 * step_per_epoch,
180 * step_per_epoch
],
values=[
lr, lr * 0.8, lr * 0.6, lr * 0.4, lr * 0.2, lr * 0.1
]),
beta1=0.5,
name="g_A")
optimizer.minimize(self.g_loss_A, parameter_list=vars)
class GBTrainer():
def __init__(self, input_A, input_B):
self.program = fluid.default_main_program().clone()
with fluid.program_guard(self.program):
self.fake_B = build_generator_resnet_9blocks(input_A, name="g_A")
self.fake_A = build_generator_resnet_9blocks(input_B, name="g_B")
self.cyc_A = build_generator_resnet_9blocks(self.fake_B, "g_B")
self.cyc_B = build_generator_resnet_9blocks(self.fake_A, "g_A")
self.infer_program = self.program.clone()
diff_A = fluid.layers.abs(
fluid.layers.elementwise_sub(
x=input_A, y=self.cyc_A))
diff_B = fluid.layers.abs(
fluid.layers.elementwise_sub(
x=input_B, y=self.cyc_B))
self.cyc_loss = (
fluid.layers.reduce_mean(diff_A) +
fluid.layers.reduce_mean(diff_B)) * cycle_loss_factor
self.fake_rec_A = build_gen_discriminator(self.fake_A, "d_A")
disc_loss_A = fluid.layers.reduce_mean(
fluid.layers.square(self.fake_rec_A - 1))
self.g_loss_B = fluid.layers.elementwise_add(self.cyc_loss,
disc_loss_A)
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and var.name.startswith("g_B"):
vars.append(var.name)
self.param = vars
lr = 0.0002
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.piecewise_decay(
boundaries=[
100 * step_per_epoch, 120 * step_per_epoch,
140 * step_per_epoch, 160 * step_per_epoch,
180 * step_per_epoch
],
values=[
lr, lr * 0.8, lr * 0.6, lr * 0.4, lr * 0.2, lr * 0.1
]),
beta1=0.5,
name="g_B")
optimizer.minimize(self.g_loss_B, parameter_list=vars)
class DATrainer():
def __init__(self, input_A, fake_pool_A):
self.program = fluid.default_main_program().clone()
with fluid.program_guard(self.program):
self.rec_A = build_gen_discriminator(input_A, "d_A")
self.fake_pool_rec_A = build_gen_discriminator(fake_pool_A, "d_A")
self.d_loss_A = (fluid.layers.square(self.fake_pool_rec_A) +
fluid.layers.square(self.rec_A - 1)) / 2.0
self.d_loss_A = fluid.layers.reduce_mean(self.d_loss_A)
optimizer = fluid.optimizer.Adam(learning_rate=0.0002, beta1=0.5)
optimizer._name = "d_A"
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and var.name.startswith("d_A"):
vars.append(var.name)
self.param = vars
lr = 0.0002
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.piecewise_decay(
boundaries=[
100 * step_per_epoch, 120 * step_per_epoch,
140 * step_per_epoch, 160 * step_per_epoch,
180 * step_per_epoch
],
values=[
lr, lr * 0.8, lr * 0.6, lr * 0.4, lr * 0.2, lr * 0.1
]),
beta1=0.5,
name="d_A")
optimizer.minimize(self.d_loss_A, parameter_list=vars)
class DBTrainer():
def __init__(self, input_B, fake_pool_B):
self.program = fluid.default_main_program().clone()
with fluid.program_guard(self.program):
self.rec_B = build_gen_discriminator(input_B, "d_B")
self.fake_pool_rec_B = build_gen_discriminator(fake_pool_B, "d_B")
self.d_loss_B = (fluid.layers.square(self.fake_pool_rec_B) +
fluid.layers.square(self.rec_B - 1)) / 2.0
self.d_loss_B = fluid.layers.reduce_mean(self.d_loss_B)
optimizer = fluid.optimizer.Adam(learning_rate=0.0002, beta1=0.5)
vars = []
for var in self.program.list_vars():
if fluid.io.is_parameter(var) and var.name.startswith("d_B"):
vars.append(var.name)
self.param = vars
lr = 0.0002
optimizer = fluid.optimizer.Adam(
learning_rate=fluid.layers.piecewise_decay(
boundaries=[
100 * step_per_epoch, 120 * step_per_epoch,
140 * step_per_epoch, 160 * step_per_epoch,
180 * step_per_epoch
],
values=[
lr, lr * 0.8, lr * 0.6, lr * 0.4, lr * 0.2, lr * 0.1
]),
beta1=0.5,
name="d_B")
optimizer.minimize(self.d_loss_B, parameter_list=vars)