]
dataloaders = {}
dataloaders['train'] = DataLoader(dataset['train'],
batch_size=batch_size['train'],
sampler=ImbalancedDatasetSampler(
dataset['train']),
num_workers=0)
dataloaders['val'] = DataLoader(dataset['val'],
batch_size=batch_size['val'],
shuffle=True,
num_workers=0)
img_model = torch.load('../model/tsx.pth')
# spe_model = torch.load('../model/slc_spexy_cae_2.pth')
net_joint = network.SLC_joint2(cate_num)
net_joint = get_pretrained(img_model, net_joint)
# net_joint.load_state_dict(torch.load('../model/slc_joint_deeper_' + str(datasetnum) + '_F_con.pth'))
net_joint.to(device)
epoch_num = 7000
i = 0
parameter_list = param_setting_jointmodel2(model=net_joint)
optimizer = optim.SGD(parameter_list, lr=0.01, weight_decay=0.0005)
lr_list = [param_group['lr'] for param_group in optimizer.param_groups]
loss_weight = torch.Tensor(loss_weight).to(device)
loss_func = nn.CrossEntropyLoss(weight=loss_weight)
writer = SummaryWriter('../log/' + save_model_path.split('/')[-1] + 'log')
def get_train_features_transmat(data_dir):
# data_dir = 'slc_train_3'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# device = torch.device('cpu')
txt_file = '../data/' + data_dir + '.txt'
batch_size = 10
cate_num = 8
spe_transform = transforms.Compose([
# transform_data.Normalize_spe_xy(),
transform_data.Numpy2Tensor()
])
img_transform = transforms.Compose([
transform_data.Normalize_img(),
transform_data.Numpy2Tensor_img(3)
])
dataset = slc_dataset.SLC_img_spe4D(txt_file=txt_file,
img_dir='../data/slc_data/',
spe_dir='../data/spexy_data_3/',
img_transform=img_transform,
spe_transform=spe_transform)
dataloaders = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
img_model = torch.load('../model/tsx.pth')
net_joint = network.SLC_joint2(cate_num)
net_joint = get_pretrained(img_model, net_joint)
net_joint.to(device)
np_label = np.zeros(0)
np_img_feature = np.zeros([0, 128, 16, 16])
np_spe_feature = np.zeros([0, 128, 16, 16])
for data in dataloaders:
img_data = data['img'].to(device)
spe_data = data['spe'].to(device)
labels = data['label'].to(device)
img_features = net_joint.pre_img_features(img_data)
spe_features = net_joint.pre_spe_features(spe_data)
np_label = np.concatenate((np_label, labels.cpu().data.numpy()))
np_img_feature = np.concatenate((np_img_feature, img_features.cpu().data.numpy()), axis=0)
np_spe_feature = np.concatenate((np_spe_feature, spe_features.cpu().data.numpy()), axis=0)
N = len(np_label)
np_img_feature = np_img_feature.reshape([N, 128, 256])
np_spe_feature = np_spe_feature.reshape([N, 128, 256])
transmat_img_feature = np.zeros([0, 7, 128])
transmat_spe_feature = np.zeros([0, 7, 128])
output_img_feature = np.zeros([0, 7, N])
output_spe_feature = np.zeros([0, 7, N])
# _, _, x, y = img_features.shape
for i in range(256):
per_img_feature = np_img_feature[:, :, i].T
per_spe_feature = np_spe_feature[:, :, i].T
per_output_img_feature, per_output_spe_feature, per_transmat_img_feature, per_transmat_spe_feature = \
dcaFuse.dcaFuse(per_img_feature, per_spe_feature, np_label)
transmat_img_feature = np.concatenate((transmat_img_feature, per_transmat_img_feature.reshape([1, 7, 128])),
axis=0)
transmat_spe_feature = np.concatenate((transmat_spe_feature, per_transmat_spe_feature.reshape([1, 7, 128])),
axis=0)
output_img_feature = np.concatenate((output_img_feature, per_output_img_feature.reshape([1, 7, N])), axis=0)
output_spe_feature = np.concatenate((output_spe_feature, per_output_spe_feature.reshape([1, 7, N])), axis=0)
np.save('../data/' + data_dir + '_img_features.npy',
output_img_feature.reshape([16, 16, 7, N]).transpose(3, 2, 0, 1))
np.save('../data/' + data_dir + '_spe_features.npy',
output_spe_feature.reshape([16, 16, 7, N]).transpose(3, 2, 0, 1))
np.save('../data/' + data_dir + '_transmat_img.npy', transmat_img_feature.reshape([16, 16, 7, 128]))
np.save('../data/' + data_dir + '_transmat_spe.npy', transmat_spe_feature.reshape([16, 16, 7, 128]))
np.save('../data/' + data_dir + '_label.npy', np_label)