def main(config):
word2vec_model = gensim.models.Word2Vec.load(config.pretrained_word_vector)
word2vec_model.wv["<pad>"] = np.zeros(word2vec_model.wv.vector_size)
word2vec_model.wv["<unk>"] = np.zeros(word2vec_model.wv.vector_size)
preprocessor = Preprocessor(word2vec_model)
test_dataloader = get_dataloader(
config.test_data, config.max_len, preprocessor, config.batch_size
)
net = CNN(word2vec_model.wv, config)
checkpoint = torch.load(config.ckpt_path)
net.load_state_dict(checkpoint["state_dict"])
trainer = pl.Trainer(
distributed_backend=config.distributed_backend,
gpus=config.gpus,
)
res = trainer.test(net, test_dataloader)
def main(args):
# Build Models
encoder = CNN(args.hidden_size)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = LSTM(args.embed_size, args.hidden_size,
len(vocab), args.num_layers)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
decoder.load_state_dict(torch.load(args.decoder_path))
# load data set
is_training = True
testing_data = IDDataset(not is_training)
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
test_acc = evaluation(testing_data, encoder, decoder)
print("Accuracy is %.4f" % test_acc)
def main():
parser = argparse.ArgumentParser(description='pytorch example: MNIST')
parser.add_argument('--batch',
'-b',
type=int,
default=100,
help='Number of images in each mini-batch')
parser.add_argument('--model',
'-m',
default='model.pth',
help='Network Model')
args = parser.parse_args()
batch_size = args.batch
print('show training log')
df = pd.read_csv('train.log')
plt.plot(df['epoch'], df['train/accuracy'], label='train/acc.', marker="o")
plt.plot(df['epoch'], df['test/accuracy'], label='test/acc.', marker="o")
plt.legend(loc='lower right')
plt.ylim([0.8, 1.0])
plt.savefig('accuracy.png')
plt.show()
transform = transforms.Compose([
transforms.ToTensor(), # transform to torch.Tensor
transforms.Normalize(mean=(0.5, ), std=(0.5, ))
])
trainset = torchvision.datasets.CIFAR10(root='../cifar10_root',
train=True,
download=True,
transform=transform)
testset = torchvision.datasets.CIFAR10(root='../cifar10_root',
train=False,
download=True,
transform=transform)
dataset = trainset + testset
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=2)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('device:', device)
# Load & Predict Test
param = torch.load('model.pth')
net = CNN() #読み込む前にクラス宣言が必要
net.to(device) # for GPU
net.load_state_dict(param)
true_list = []
pred_list = []
with torch.no_grad():
for data in dataloader:
images, labels = data
true_list.extend(labels.tolist())
images, labels = images.to(device), labels.to(device) # for GPU
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
pred_list.extend(predicted.tolist())
acc = accuracy_score(true_list, pred_list)
print('Predict... all data acc.: {:.3f}'.format(acc))
confmat = confusion_matrix(y_true=true_list, y_pred=pred_list)
fig, ax = plt.subplots(figsize=(6, 6))
ax.matshow(confmat, cmap=plt.cm.Purples, alpha=0.8)
for i in range(confmat.shape[0]):
for j in range(confmat.shape[1]):
if confmat[i, j] > 0:
ax.text(x=j, y=i, s=confmat[i, j], va='center', ha='center')
plt.xlabel('predicted label')
plt.ylabel('true label')
plt.tight_layout()
plt.savefig('confusion_matrix.png')
plt.show()
import torch
from net import MLP, CNN #
from torchvision import datasets, transforms
from sklearn.metrics import multilabel_confusion_matrix
#
test_loader = torch.utils.data.DataLoader(datasets.FashionMNIST(
'./fashionmnist_data/',
train=False,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])),
batch_size=1,
shuffle=True)
model = CNN()
device = torch.device('cpu')
model = model.to(device)
model.load_state_dict(torch.load('output/CNN.pt'))
model.eval()
pres = []
labels = []
i = 0
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
pred = output.argmax(
dim=1, keepdim=True) # get the index of the max log-probability
pres.append(pred[0][0].item())
labels.append(target[0].item())
mcm = multilabel_confusion_matrix(labels, pres) #mcm
print(mcm)
orig = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
#orig = cv2.resize(orig, (IMG_SIZE, IMG_SIZE))
img = orig.copy().astype(np.float32)
perturbation = np.empty_like(orig)
mean = [0.5]
std = [0.5]
img /= 255.0
img = (img - mean) / std
# load model
model1 = CNN(1, 10)
saved1 = torch.load('relu.pkl', map_location='cpu')
model1.load_state_dict(saved1)
model1.eval()
criterion = nn.CrossEntropyLoss()
device = 'cuda' if gpu else 'cpu'
# prediction before attack
inp = Variable(
torch.from_numpy(img).to(device).float().unsqueeze(0).unsqueeze(0),
requires_grad=True)
out1 = model1(inp)
pred1 = np.argmax(out1.data.cpu().numpy())