outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# Running loss update
running_loss += loss.item()
# Print the running loss every 1000 mini-batches
if i % 1000 == 999:
print('Epoch : %2d, mini-batch : %4d, loss : %.3f' %
(epoch, i + 1, running_loss / 1000))
running_loss = 0.0
# Set the model to evaluation mode
model.eval()
# Compute training set accuracy
training_correct = 0
training_total = 0
with torch.no_grad():
for data in trainloader:
images, labels = data
outputs = model(images)
predicted = torch.argmax(outputs.data, 1)
training_correct += (predicted == labels).sum().item()
training_total += labels.size(0)
training_accuracy = 100. * training_correct / training_total
# Compute test set accuracy
test_correct = 0
def main(args):
#hyper parameter
end_epoch = 200
lr = 0.001
beta1 = 0.5
beta2 = 0.99
gpu = 0
#set model
model = MyNet()
#set GPU or CPU
if gpu >= 0 and torch.cuda.is_available():
device = 'cuda:{}'.format(gpu)
else:
device = 'cpu'
model.to(device)
model.load_state_dict(
torch.load("models/" + args.model + "/" + str(args.epoch - 1) +
".pth"))
model.eval()
criteria = nn.CrossEntropyLoss()
dataset = MyDataset("data/", is_train=False)
test_loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=True)
with torch.no_grad():
epoch_loss = 0
epoch_acc = 0
if not os.path.exists("result/" + args.model + "/true_favo/"):
os.makedirs("result/" + args.model + "/true_favo/")
os.makedirs("result/" + args.model + "/true_no_favo/")
os.makedirs("result/" + args.model + "/false_favo/")
os.makedirs("result/" + args.model + "/false_no_favo/")
for i, data in enumerate(test_loader):
print("\riteration: {}".format(i), end="")
inputs, labels = data
outputs = model(inputs.to(device))
_, preds = torch.max(outputs.data, 1)
loss = criteria(outputs, labels.to(device))
favo_preds = preds.to('cpu')
if favo_preds == labels:
if favo_preds == 1:
save_image(
inputs, "result/" + args.model + "/true_favo/" +
str(i) + ".jpg")
elif favo_preds == 0:
save_image(
inputs, "result/" + args.model + "/true_no_favo/" +
str(i) + ".jpg")
else:
if favo_preds == 1:
save_image(
inputs, "result/" + args.model + "/false_favo/" +
str(i) + ".jpg")
elif favo_preds == 0:
save_image(
inputs, "result/" + args.model + "/false_no_favo/" +
str(i) + ".jpg")
epoch_loss += loss.data.to('cpu') * inputs.size(0)
epoch_acc += torch.sum(favo_preds == labels.data)
epoch_loss /= len(test_loader)
epoch_acc = epoch_acc / float(len(test_loader))
print("[Loss: {}] [Acc: {}]".format(epoch_loss, epoch_acc))
