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()
import torch
import dataload
from net import net
net.load_state_dict(torch.load('./try/model_trained.pth'))
# net.load_state_dict(torch.load('./try/model_trained_2.pth'))
# outputs = net(images)
# _, predicted = torch.max(outputs, 1)
# print('Predicted: ', ' '.join('%5s' % load.classes[predicted[j]] for j in range(4)))
class_correct = list(0. for i in range(6))
class_total = list(0. for i in range(6))
# print(class_correct) # [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
with torch.no_grad():
for data in dataload.val_dataloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs, 1)
# print(predicted) # tensor([3, 3, 3, 3, 0, 0, 3, 3]) # situation of batch=8, also apply to below
# tensor([0, 3, 4, 3, 4, 3, 3, 3])
# tensor([0, 3, 3, 3, 4, 0, 3, 0])
# ....
c = (predicted == labels).squeeze()
# print(c) # tensor([False, False, True, True, True, False, False, False])
# tensor([ True, True, False, True, True, False, True, True])
# tensor([False, True, False, False, False, True, False, True])
# ....
for i in range(
4): # ratio[0.1]时，test总数=251，除4余3，因此会报错index out of range.
import os, torch, pandas as pd
from sklearn.metrics import mean_squared_error
from find_token import find_token
from test_set import test_set
from net import net
net.eval()
batch_sizes, test_losses = [], []
for model in os.listdir('../diff_batch_sizes'):
#Get batch size
batch_size = int(find_token(model, 'batch_size=', '__lr='))
#Load the model
net.load_state_dict(
torch.load('../diff_batch_sizes/' + model,
map_location=torch.device('cpu')))
test_predictions = []
for start_index in range(0, test_set.shape[0] - batch_size + 1,
batch_size):
#get batches
X = test_set[start_index:start_index + batch_size, :-5].view(
batch_size, 1, 285)
y = test_set[start_index:start_index + batch_size, -5:]
#forward pass
outputs = net(X).view(batch_size, 5).detach()
test_predictions.append(outputs)
predictions = torch.cat(test_predictions, dim=0).numpy()
batch_sizes.append(batch_size)