def loadModel(self, filepath):
'''
加载权重参数
:param filepath:
:return:
'''
model = Net()
model.cuda()
model.load_state_dict(torch.load(filepath))
return model
test_data = dataset(test_set)
train_loader = DataLoader(dataset=train_data, num_workers=4)
test_loader = DataLoader(dataset=test_data, num_workers=4)
##### bulid a validation model #####
device = torch.device('cuda')
val_net = Net(ks=3, stride=1).to(device)
print(val_net)
train_acc = val(train_loader, val_net, pth)
test_acc = val(test_loader, val_net, pth)
print('Train acc', train_acc)
print('Test acc', test_acc)
val_net.load_state_dict(torch.load(pth, map_location=device))
val_net.eval()
visul_data = pd.read_csv(ROOT_DIR + test_scv)
for v in visul:
img = cv2.imread(ROOT_DIR + '/images/' + v)
start = test_set['name'].index(v)
step = test_set['name'].count(v)
# predict each picture patch
for i in range(step):
row = visul_data.iloc[start + i]
input = test_set['image'][start + i]
input = np.swapaxes(input, 0, 1).reshape(3, IMG_SIZE[1],
IMG_SIZE[0]) / 255
input = torch.FloatTensor(input)
