def test_net(test_loader=None, path=None, batch_size=128):
#run test loop here
n_batches = len(test_loader)
model = torch.load(path)
net = model['model']
net.load_state_dict(model['state_dict'])
for par in net.parameters():
par.requires_grad = False
net.eval()
#writing results to spreadsheet
fname = "test_pred.csv"
f_out = open(fname, "w")
wrt = csv.writer(f_out)
net = net.cpu()
#testing metrics
corr_cnt = 0
total_iter = 0
for data in test_loader:
[inputs, labels, snr] = data
snr = snr.numpy()
#inputs, labels,snr = Variable(inputs), Variable(labels), Variable(snr)
pred = net(inputs.float()).numpy()
pred = np.argmax(pred, axis=1)
labels = np.argmax(labels.numpy(), axis=1)
for s, p, l in zip(snr, pred, labels):
if (p == l):
corr_cnt += 1
wrt.writerow([s, p, l])
total_iter += 1
print("Test done, accr = :" + str(corr_cnt / total_iter))
f_out.close()
def test_net(test_loader=None,
path='model.pt',
batch_size=128,
fname=None,
a=8,
b=12,
c=20):
n_batches = len(test_loader)
model = torch.load(path)
net = model['model']
net.load_state_dict(model['state_dict'])
for par in net.parameters():
par.requires_grad = False
net.eval()
net = net.float()
net = net.to('cuda')
#writing results to spreadsheet
if fname is None:
fname = 'test_pred.csv'
f_out = open(fname, "w")
wrt = csv.writer(f_out)
#testing metrics
corr_cnt = 0
total_iter = 0
run_max = 0
for i in range(20, 30):
for j in range(40, 60):
for k in range(70, 80):
for data in test_loader:
[inputs, labels, snr] = data
inputs, labels = Variable(inputs).to('cuda'), Variable(
labels)
pred = net(inputs.float())
snr = snr.numpy()
pred = pred.cpu().numpy()
labels = np.argmax(labels.numpy(), axis=1)
for s, p, l in zip(snr, pred, labels):
#wrt.writerow([s,p,l])
#wrt.writerow([p,l])
#p = bisect.bisect_left([0.25,0.5,0.75],p)
p = bisect.bisect_left(
[float(i / 100),
float(j / 100),
float(k / 100)], p)
if (p == l):
corr_cnt += 1
total_iter += 1
acc = corr_cnt / total_iter
if (run_max < acc):
run_max = acc
print("Test done, accr = :" + str(acc))
#print("i" + str(float(i/100)))
#print("j" + str(float(j/100)))
#print("k" + str(float(k/100)))
wrt.writerow([i, j, k, acc])
print(run_max)
f_out.close()
#get loss
loss = criterion(outputs, y)
#backward pass
loss.backward()
optimizer.step()
running_loss += loss.item()
if epoch % 50 == 49 or epoch == 0: # print every 10 epochs
print('{} epoch {} loss: {}'.format(
'', epoch if epoch > len(losses) else len(losses), running_loss))
losses.append(running_loss)
from plot_losses import plot_losses
plot_losses(losses)
from sklearn.metrics import mean_squared_error
from predict import predict
from plot_execution_times import plot_execution_times
from test_set import test_set
test_set = test_set.to(device)
net.eval()
predictions = predict(net, 32, test_set)
print('Test Loss:', mean_squared_error(test_set[:, -5:], predictions))
plot_execution_times(predictions, test_set[:, -5:])