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ІО-23 Шмуляр Олег 2025-11-18 16:06:17 +02:00
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test.py
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@ -1,67 +1,191 @@
import tensorflow as tf
import numpy as np
from tensorflow import keras as k
from tensorflow.keras import layers as kl
from tensorflow.keras import models as km
from tensorflow.keras import optimizers as ko
CONV_SIZE = 128
MPPT_SIZE = 1024
DRPT_RATE = 0.3
TOUT_AMNT = 2
def conv(i, n, s, t = (1, 1)):
c = kl.Conv2D(n, s, padding = 'same', strides = t)(i)
b = kl.BatchNormalization()(c)
return kl.Activation('relu')(b)
def avg(i, s):
return kl.AveragePooling2D(s, padding = 'same')(i)
def max(i, s):
return kl.MaxPooling2D(s, padding = 'same')(i)
def generate_start(i):
r1 = conv(i, CONV_SIZE, (3, 3))
r2 = conv(r1, CONV_SIZE, (3, 3))
r3 = conv(r2, CONV_SIZE, (3, 3))
m1 = kl.MaxPooling2D((3, 3))(r3)
r4 = conv(m1, CONV_SIZE, (1, 1))
r5 = conv(r4, CONV_SIZE, (3, 3))
return kl.MaxPooling2D((3, 3))(r5)
def generate_type_a(i):
a11 = conv(i, CONV_SIZE, (1, 1), (3, 3))
a21 = avg(i, (3, 3))
a22 = conv(a21, CONV_SIZE, (1, 1))
a31 = conv(i, CONV_SIZE, (1, 1))
a32 = conv(a31, CONV_SIZE, (3, 3), (3, 3))
a41 = conv(i, CONV_SIZE, (1, 1))
a42 = conv(a41, CONV_SIZE, (3, 3))
a43 = conv(a42, CONV_SIZE, (3, 3), (3, 3))
return kl.Concatenate()([a11, a22, a32, a43])
def generate_ab_bridge(i):
ab11 = conv(i, CONV_SIZE, (1, 1))
ab12 = conv(ab11, CONV_SIZE, (3, 3))
ab13 = conv(ab12, CONV_SIZE, (3, 3), (3, 3))
ab21 = conv(i, CONV_SIZE, (3, 3), (3, 3))
ab31 = max(i, (3, 3))
return kl.Concatenate()([ab13, ab21, ab31])
def generate_type_b(i):
b11 = conv(i, CONV_SIZE, (1, 1))
b12 = conv(b11, CONV_SIZE, (7, 1))
b13 = conv(b12, CONV_SIZE, (1, 7))
b14 = conv(b13, CONV_SIZE, (7, 1))
b15 = conv(b14, CONV_SIZE, (1, 7), (3, 3))
b21 = conv(i, CONV_SIZE, (1, 1))
b22 = conv(b21, CONV_SIZE, (1, 7))
b23 = conv(b22, CONV_SIZE, (7, 1), (3, 3))
b31 = conv(i, CONV_SIZE, (1, 1), (3, 3))
b41 = avg(i, (3, 3))
b42 = conv(b41, CONV_SIZE, (1, 1))
return kl.Concatenate()([b15, b23, b31, b42])
def generate_bc_bridge(i):
bc11 = conv(i, CONV_SIZE, (1, 1))
bc12 = conv(bc11, CONV_SIZE, (7, 1))
bc13 = conv(bc12, CONV_SIZE, (1, 7))
bc14 = conv(bc13, CONV_SIZE, (3, 3), (3, 3))
bc21 = conv(i, CONV_SIZE, (1, 1))
bc22 = conv(bc21, CONV_SIZE, (1, 1), (3, 3))
bc31 = max(i, (3, 3))
return kl.Concatenate()([bc14, bc22, bc31])
def generate_aux(i):
u1 = kl.AveragePooling2D((5, 5))(i)
u2 = conv(u1, CONV_SIZE, (1, 1))
u3 = kl.Flatten()(u2)
u4 = kl.Dropout(DRPT_RATE)(u3)
u5 = kl.Dense(MPPT_SIZE)(u4)
return kl.Dense(TOUT_AMNT)(u5)
def generate_type_c(i):
c11 = conv(i, CONV_SIZE, (1, 1))
c12 = conv(c11, CONV_SIZE, (3, 3))
c13 = conv(c12, CONV_SIZE, (1, 3), (3, 3))
c14 = conv(c12, CONV_SIZE, (3, 1), (3, 3))
c21 = conv(i, CONV_SIZE, (1, 1))
c22 = conv(c21, CONV_SIZE, (1, 3), (3, 3))
c23 = conv(c21, CONV_SIZE, (3, 1), (3, 3))
c31 = max(i, (3, 3))
c32 = conv(c31, CONV_SIZE, (1, 1))
c41 = conv(i, CONV_SIZE, (1, 1))
return kl.Concatenate()([c14, c23, c32, c41])
def generate_finish(i):
f1 = kl.AveragePooling2D((5, 5))(i)
f2 = conv(f1, CONV_SIZE, (1, 1))
f3 = kl.Flatten()(f2)
f4 = kl.Dropout(DRPT_RATE)(f3)
f5 = kl.Dense(MPPT_SIZE)(f4)
return kl.Dense(TOUT_AMNT)(f5)
'''
def generate_inception_model(a = 5, b = 4, c = 2):
# start
i = kl.Input(shape = (300, 300, 3))
r1 = kl.Conv2D(1024, (3, 3), padding = 'same', activation = 'relu')(i)
r2 = kl.Conv2D(1024, (5, 5), padding = 'same', activation = 'relu')(r1)
r3 = kl.Conv2D(1024, (7, 7), padding = 'same', activation = 'relu')(r2)
r1 = kl.Conv2D(CONV_SIZE, (3, 3), padding = 'same', activation = 'relu')(i)
r2 = kl.Conv2D(CONV_SIZE, (5, 5), padding = 'same', activation = 'relu')(r1)
r3 = kl.Conv2D(CONV_SIZE, (7, 7), padding = 'same', activation = 'relu')(r2)
m1 = kl.MaxPooling2D((3, 3))(r3)
r4 = kl.Conv2D(1024, (3, 3), padding = 'same', activation = 'relu')(m1)
r5 = kl.Conv2D(1024, (5, 5), padding = 'same', activation = 'relu')(r4)
r4 = kl.Conv2D(CONV_SIZE, (3, 3), padding = 'same', activation = 'relu')(m1)
r5 = kl.Conv2D(CONV_SIZE, (5, 5), padding = 'same', activation = 'relu')(r4)
ia0 = kl.MaxPooling2D((2, 2))(r5)
# a types
for k in range(a):
exec(f"ia{k}_1_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
exec(f"ia{k}_1_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
exec(f"ia{k}_3_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
exec(f"ia{k}_3_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ia{k}_3_1)")
exec(f"ia{k}_3_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
exec(f"ia{k}_3_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ia{k}_3_1)")
exec(f"ia{k}_5_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
exec(f"ia{k}_5_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ia{k}_5_1)")
exec(f"ia{k}_5_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ia{k}_5_2)")
exec(f"ia{k}_5_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
exec(f"ia{k}_5_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ia{k}_5_1)")
exec(f"ia{k}_5_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ia{k}_5_2)")
exec(f"ia{k}_7_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
exec(f"ia{k}_7_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ia{k}_7_1)")
exec(f"ia{k}_7_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ia{k}_7_2)")
exec(f"ia{k}_7_4 = kl.Conv2D(1024, (7, 1), padding = 'same', activation = 'relu')(ia{k}_7_3)")
exec(f"ia{k}_7_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(ia{k})")
exec(f"ia{k}_7_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ia{k}_7_1)")
exec(f"ia{k}_7_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ia{k}_7_2)")
exec(f"ia{k}_7_4 = kl.Conv2D(CONV_SIZE, (7, 1), padding = 'same', activation = 'relu')(ia{k}_7_3)")
exec(f"ia{k+1} = kl.Concatenate()([ia{k}_1_1, ia{k}_3_2, ia{k}_5_3, ia{k}_7_4])")
# grid size reductor 1
iab_1 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(eval(f"ia{a}"))
iab_1 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu', strides = (2,2))(eval(f"ia{a}"))
iab_2 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(eval(f"ia{a}"))
iab_3 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(iab_2)
iab_4 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(iab_3)
iab_2 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(eval(f"ia{a}"))
iab_3 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(iab_2)
iab_4 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu', strides = (2,2))(iab_3)
iab_5 = kl.MaxPooling2D((2, 2), padding = 'same')(eval(f"ia{a}"))
iab_6 = kl.Concatenate()([iab_1, iab_4, iab_5])
# b types
for k in range(b):
exec(f"ib{k}_1_1 = kl.MaxPooling2D((2, 2), padding = 'same')(iab_6)")
exec(f"ib{k}_1_2 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i)")
exec(f"ib{k}_1_2 = kl.Conv2D(CONV_SIZE, (1, 1), strides = (2,2), padding = 'same', activation = 'relu')(i)")
exec(f"ib{k}_3_1 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(iab_6)")
exec(f"ib{k}_3_1 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(iab_6)")
exec(f"ib{k}_5_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(iab_6)")
exec(f"ib{k}_5_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_5_1)")
exec(f"ib{k}_5_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_5_2)")
exec(f"ib{k}_5_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(iab_6)")
exec(f"ib{k}_5_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ib{k}_5_1)")
exec(f"ib{k}_5_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ib{k}_5_2)")
exec(f"ib{k}_7_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(iab_6)")
exec(f"ib{k}_7_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_7_1)")
exec(f"ib{k}_7_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_7_2)")
exec(f"ib{k}_7_4 = kl.Conv2D(1024, (7, 1), padding = 'same', activation = 'relu')(ib{k}_7_3)")
exec(f"ib{k}_7_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(iab_6)")
exec(f"ib{k}_7_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ib{k}_7_1)")
exec(f"ib{k}_7_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ib{k}_7_2)")
exec(f"ib{k}_7_4 = kl.Conv2D(CONV_SIZE, (7, 1), padding = 'same', activation = 'relu')(ib{k}_7_3)")
exec(f"ib{k+1} = kl.Concatenate()([ib{k}_1_2, ib{k}_3_2, ib{k}_5_3, ib{k}_7_4])")
@ -69,18 +193,18 @@ def generate_inception_model(a = 5, b = 4, c = 2):
# c types
for k in range(c):
exec(f"ic{k}_1_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
exec(f"ic{k}_1_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
exec(f"ic{k}_3_1 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
exec(f"ic{k}_3_1 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
exec(f"ic{k}_5_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
exec(f"ic{k}_5_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_5_1)")
exec(f"ic{k}_5_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_5_2)")
exec(f"ic{k}_5_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
exec(f"ic{k}_5_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ib{k}_5_1)")
exec(f"ic{k}_5_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ib{k}_5_2)")
exec(f"ic{k}_7_1 = kl.Conv2D(1024, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
exec(f"ic{k}_7_2 = kl.Conv2D(1024, (3, 1), padding = 'same', activation = 'relu')(ib{k}_7_1)")
exec(f"ic{k}_7_3 = kl.Conv2D(1024, (5, 1), padding = 'same', activation = 'relu')(ib{k}_7_2)")
exec(f"ic{k}_7_4 = kl.Conv2D(1024, (7, 1), padding = 'same', activation = 'relu')(ib{k}_7_3)")
exec(f"ic{k}_7_1 = kl.Conv2D(CONV_SIZE, (1, 1), padding = 'same', activation = 'relu')(i{'b' if k else 'a'}{k if k else a})")
exec(f"ic{k}_7_2 = kl.Conv2D(CONV_SIZE, (3, 1), padding = 'same', activation = 'relu')(ib{k}_7_1)")
exec(f"ic{k}_7_3 = kl.Conv2D(CONV_SIZE, (5, 1), padding = 'same', activation = 'relu')(ib{k}_7_2)")
exec(f"ic{k}_7_4 = kl.Conv2D(CONV_SIZE, (7, 1), padding = 'same', activation = 'relu')(ib{k}_7_3)")
exec(f"ib{k+1} = kl.Concatenate()([ib{k}_1_1, ib{k}_3_2, ib{k}_5_3, ib{k}_7_4])")
@ -88,6 +212,7 @@ def generate_inception_model(a = 5, b = 4, c = 2):
o = eval(f"ic{c}")
return tf.keras.Model(inputs = i, outputs = o)
'''
'''
@ -111,6 +236,31 @@ inception_type_a = [a_i, a_o]
tf.keras.Model(*inception_type_a)
'''
mod = generate_inception_model()
gi = kl.Input((300, 300, 3))
ds = generate_start(gi)
for _ in range(5):
ds = generate_type_a(ds)
ds = generate_ab_bridge(ds)
for _ in range(4):
ds = generate_type_b(ds)
uo = generate_aux(ds)
go = generate_bc_bridge(ds)
for _ in range(2):
go = generate_type_c(go)
go = generate_finish(go)
r = kl.Concatenate()([uo, go])
mod = k.Model(inputs = gi, outputs = r)
mod.summary()
mod.compile(optimizer = ko.Lion(learning_rate = 0.001))