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adjscc_cifar10.py
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adjscc_cifar10.py
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from util_channel import Channel
from util_module import Attention_Encoder, Attention_Decoder
from tensorflow.keras.layers import Input, Lambda
from tensorflow.keras import Model
from tensorflow.keras.optimizers import Adam
import tensorflow as tf
import numpy as np
import argparse
from dataset import dataset_cifar10
import os
import json
AUTOTUNE = tf.data.experimental.AUTOTUNE
def train(args, model):
epoch_list = []
loss_list = []
val_loss_list = []
min_loss = 10 ** 8
filename = os.path.basename(__file__).split('.')[0] + '_' + str(args.channel_type) + '_tcn' + str(
args.transmit_channel_num) + '_snrdb' + str(args.snr_low_train) + 'to' + str(
args.snr_up_train) + '_bs' + str(args.batch_size) + '_lr' + str(args.learning_rate)
model_path = args.model_dir + filename + '.h5'
if args.load_model_path != None:
model.load_weights(args.load_model_path)
for epoch in range(0, args.epochs):
if args.channel_type == 'awgn':
(train_ds, train_nums), (test_ds, test_nums) = dataset_cifar10.get_dataset_snr_range(args.snr_low_train,
args.snr_up_train)
elif args.channel_type == 'slow_fading' or args.channel_type == 'slow_fading_eq':
(train_ds, train_nums), (test_ds, test_nums) = dataset_cifar10.get_dataset_snr_range_and_h(
args.snr_low_train, args.snr_up_train)
train_ds = train_ds.shuffle(buffer_size=train_nums)
train_ds = train_ds.batch(args.batch_size)
test_ds = test_ds.shuffle(buffer_size=test_nums)
test_ds = test_ds.batch(args.batch_size)
train_ds = train_ds.prefetch(buffer_size=AUTOTUNE)
h = model.fit(train_ds, epochs=1, steps_per_epoch=(
train_nums // args.batch_size if train_nums % args.batch_size == 0 else train_nums // args.batch_size + 1),
validation_data=test_ds, validation_steps=(
test_nums // args.batch_size if test_nums % args.batch_size == 0 else test_nums // args.batch_size + 1))
his = h.history
loss = his.get('loss')[0]
val_loss = his.get('val_loss')[0]
if val_loss < min_loss:
min_loss = val_loss
model.save_weights(model_path)
print('Epoch:', epoch + 1, ',loss=', loss, 'val_loss:', val_loss, 'save')
else:
print('Epoch:', epoch + 1, ',loss=', loss, 'val_loss:', val_loss)
epoch_list.append(epoch)
loss_list.append(loss)
val_loss_list.append(val_loss)
with open(args.loss_dir + filename + '.json', mode='w') as f:
json.dump({'epoch': epoch_list, 'loss': loss_list, 'val_loss': val_loss_list}, f)
def eval(args, model):
filename = os.path.basename(__file__).split('.')[0] + '_' + str(args.channel_type) + '_tcn' + str(
args.transmit_channel_num) + '_snrdb' + str(args.snr_low_eval) + 'to' + str(
args.snr_up_eval) + '_bs' + str(args.batch_size) + '_lr' + str(args.learning_rate)
model_path = args.model_dir + filename + '.h5'
model.load_weights(model_path)
snr_list = []
mse_list = []
psnr_list = []
for snrdb in range(args.snr_low_eval, args.snr_up_eval + 1):
imse = []
# test 10 times each snr
for i in range(0, 10):
if args.channel_type == 'awgn':
(_, _), (test_ds, test_nums) = dataset_cifar10.get_dataset_snr(snrdb)
elif args.channel_type == 'slow_fading' or args.channel_type == 'slow_fading_eq':
(_, _), (test_ds, test_nums) = dataset_cifar10.get_dataset_snr_and_h(snrdb)
test_ds = test_ds.shuffle(buffer_size=test_nums)
test_ds = test_ds.batch(args.batch_size)
mse = model.evaluate(test_ds)
imse.append(mse)
mse = np.mean(imse)
psnr = 10 * np.log10(255 ** 2 / mse)
snr_list.append(snrdb)
mse_list.append(mse)
psnr_list.append(psnr)
with open(args.eval_dir + filename + '.json', mode='w') as f:
json.dump({'snr': snr_list, 'mse': mse_list, 'psnr': psnr_list}, f)
def eval_burst(args):
input_imgs = Input(shape=(32, 32, 3))
input_snrdb = Input(shape=(1,))
input_b_prob = Input(shape=(1,))
input_b_stddev = Input(shape=(1,))
normal_imgs = Lambda(lambda x: x / 255, name='normal')(input_imgs)
encoder = Attention_Encoder(normal_imgs, input_snrdb, args.transmit_channel_num)
rv = Channel(channel_type='burst')(encoder, input_snrdb, b_prob=input_b_prob, b_stddev=input_b_stddev)
decoder = Attention_Decoder(rv, input_snrdb)
rv_imgs = Lambda(lambda x: x * 255, name='denormal')(decoder)
model = Model(inputs=[input_imgs, input_snrdb, input_b_prob, input_b_stddev], outputs=rv_imgs)
model.compile(Adam(args.learning_rate), 'mse')
filename = os.path.basename(__file__).split('.')[0] + '_' + str(args.channel_type) + '_tcn' + str(
args.transmit_channel_num) + '_snrdb' + str(args.snr_low_eval) + 'to' + str(
args.snr_up_eval) + '_bs' + str(args.batch_size) + '_lr' + str(args.learning_rate)
model_path = args.model_dir + filename + '.h5'
model.load_weights(model_path)
prob_list = []
psnr_list = []
for b_prob in np.arange(0, 0.225, 0.025):
imse = []
# test 10 times each snr
for i in range(0, 10):
(_, _), (test_ds, test_nums) = dataset_cifar10.get_test_dataset_burst(args.b_snr_eval, b_prob,
args.input_b_stddev)
test_ds = test_ds.shuffle(buffer_size=test_nums)
test_ds = test_ds.batch(args.batch_size)
mse = model.evaluate(test_ds)
imse.append(mse)
mse = np.mean(imse)
psnr = 10 * np.log10(255 ** 2 / mse)
prob_list.append(b_prob)
psnr_list.append(psnr)
with open(args.eval_dir + 'snr_evaldb' + str(args.snr_eval) + '_burst_sigma' + str(
args.b_sigma) + filename + '.json', mode='w') as f:
json.dump({'prob': prob_list, 'psnr': psnr_list}, f)
def main(args):
# construct encoder-decoder model
input_imgs = Input(shape=(32, 32, 3))
input_snrdb = Input(shape=(1,))
input_h_real = Input(shape=(1,))
input_h_imag = Input(shape=(1,))
normal_imgs = Lambda(lambda x: x / 255, name='normal')(input_imgs)
encoder = Attention_Encoder(normal_imgs, input_snrdb, args.transmit_channel_num)
if args.channel_type == 'awgn':
rv = Channel(channel_type='awgn')(encoder, input_snrdb)
elif args.channel_type == 'slow_fading':
rv = Channel(channel_type='slow_fading')(encoder, input_snrdb, input_h_real, input_h_imag)
elif args.channel_type == 'slow_fading_eq':
rv = Channel(channel_type='slow_fading_eq')(encoder, input_snrdb, input_h_real, input_h_imag)
decoder = Attention_Decoder(rv, input_snrdb)
rv_imgs = Lambda(lambda x: x * 255, name='denormal')(decoder)
if args.channel_type == 'awgn':
model = Model(inputs=[input_imgs, input_snrdb], outputs=rv_imgs)
elif args.channel_type == 'slow_fading' or args.channel_type == 'slow_fading_eq':
model = Model(inputs=[input_imgs, input_snrdb, input_h_real, input_h_imag], outputs=rv_imgs)
model.compile(Adam(args.learning_rate), 'mse')
model.summary()
if args.command == 'train':
train(args, model)
elif args.command == 'eval':
eval(args, model)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("command", help='trains/eval/eval_burst')
parser.add_argument("-ct", '--channel_type', help="awgn/slow_fading/slow_fading_eq/burst")
parser.add_argument("-md", '--model_dir', help="dir for model", default='model/')
parser.add_argument("-lmp", '--load_model_path', help="model path for loading")
parser.add_argument("-bs", "--batch_size", help="Batch size for training", default=128, type=int)
parser.add_argument("-e", "--epochs", help="epochs for training", default=1280, type=int)
parser.add_argument("-lr", "--learning_rate", help="learning_rate for training", default=0.0001, type=float)
parser.add_argument("-tcn", "--transmit_channel_num", help="transmit_channel_num for djscc model", default=16,
type=int)
parser.add_argument("-snr_low_train", "--snr_low_train", help="snr_low for training", default=0, type=int)
parser.add_argument("-snr_up_train", "--snr_up_train", help="snr_up for training", default=20, type=int)
parser.add_argument("-snr_low_eval", "--snr_low_eval", help="snr_low for evaluation", default=0, type=int)
parser.add_argument("-snr_up_eval", "--snr_up_eval", help="snr_up for evaluation", default=20, type=int)
parser.add_argument("-ldd", "--loss_dir", help="loss_dir for training", default='loss/')
parser.add_argument("-ed", "--eval_dir", help="eval_dir", default='eval/')
parser.add_argument("-b_snr_eval", "--burst_snr_eval", help="snr_eval for eval_burst", default=10, type=int)
parser.add_argument("-b_stddev", "--burst_standard_derivation", help="burst_standard_derivation for eval_burst",
default=0., type=float)
global args
args = parser.parse_args()
print("#######################################")
print("Current execution paramenters:")
for arg, value in sorted(vars(args).items()):
print("{}: {}".format(arg, value))
print("#######################################")
main(args)