def test(args):
print(args.save_dir)
model_args, data_args = load_args(Path(args.save_dir))
assert not model_args.modelfree, "Code only evaluates on model based models"
saver = ModelSaver(Path(args.save_dir), None)
power_constraint = PowerConstraint()
possible_inputs = get_md_set(model_args.md_len)
# TODO: change to batch size and batch per epoch to 1000
data_args.batch_size = 5000
data_args.batches_per_epoch = 1000
dataset_size = data_args.batch_size * data_args.batches_per_epoch
loader = InputDataloader(data_args.batch_size, data_args.block_length,
dataset_size)
loader = loader.example_generator()
SNRs = [.5, 1, 2, 3, 4]
BER = []
loss = []
for SNR in SNRs:
print(f"Testing {SNR} SNR level")
data_args.SNR = SNR
accuracy = []
losses = []
print(data_args.channel)
print(model_args.modelfree)
channel = get_channel(data_args.channel, model_args.modelfree,
data_args)
model = AutoEncoder(model_args, data_args, power_constraint, channel,
possible_inputs)
saver.load(model)
for step in tqdm(range(data_args.batches_per_epoch)):
msg = next(loader)
metrics = model.trainable_encoder.test_on_batch(msg, msg)
losses.append(metrics[0])
accuracy.append(metrics[1])
mean_loss = sum(losses) / len(losses)
mean_BER = 1 - sum(accuracy) / len(accuracy)
loss.append(mean_loss)
BER.append(mean_BER)
print(f"mean BER: {mean_BER}")
print(f"mean loss: {mean_loss}")
# create plots for results
plt.plot(SNRs, BER)
plt.ylabel("BER")
plt.xlabel("SNR")
plt.yscale('log')
plt.savefig('figures/AWGN_modelaware.png')
plt.show()
def test(args):
model_dict = load_model_dict(Path(args.model_dict_path))
BER = []
loss = []
noises = []
for noise, save_dir in model_dict.items():
model_args, data_args = load_args(Path(save_dir))
assert model_args.modelfree, "Code only evaluates on model free"
saver = ModelSaver(Path(save_dir), None)
power_constraint = PowerConstraint()
possible_inputs = get_md_set(model_args.md_len)
# TODO: change to batch size and batch per epoch to 1000
data_args.batch_size = 100
data_args.batches_per_epoch = 100
dataset_size = data_args.batch_size * data_args.batches_per_epoch
loader = InputDataloader(data_args.batch_size, data_args.block_length,
dataset_size)
loader = loader.example_generator()
if data_args.channel == "AWGN":
assert float(noise) == data_args.SNR
else:
assert float(noise) == data_args.epsilon
print(f"Testing {noise} noise level")
accuracy = []
losses = []
channel = get_channel(data_args.channel, model_args.modelfree,
data_args)
model = AutoEncoder(model_args, data_args, power_constraint, channel,
possible_inputs)
channel = get_channel(data_args.channel, model_args.modelfree,
data_args)
model = AutoEncoder(model_args, data_args, power_constraint, channel,
possible_inputs)
saver.load(model)
for step in tqdm(range(data_args.batches_per_epoch)):
msg = next(loader)
metrics = model.trainable_encoder.test_on_batch(msg, msg)
losses.append(metrics[0])
accuracy.append(metrics[1])
mean_loss = sum(losses) / len(losses)
mean_BER = 1 - sum(accuracy) / len(accuracy)
loss.append(mean_loss)
BER.append(mean_BER)
noises.append(noise)
print(f"mean BER: {mean_BER}")
print(f"mean loss: {mean_loss}")
# create plots for results
plt.plot(noises, BER, 'b--')
plt.plot(noises, BER, 'bx')
plt.ylabel("BER")
plt.xlabel("noise")
plt.yscale('log')
plt.ylim([1e-6, 1.0])
plt.savefig("figures/figure.png")