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from sklearn.metrics import confusion_matrix
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import geniter
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import record
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import torch_optimizer as optim2
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import Utils
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from torchsummary import summary
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# # Setting Params
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parser = argparse.ArgumentParser(description='Training for HSI')
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parser.add_argument(
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'-d', '--dataset', dest='dataset', default='IN', help="Name of dataset.")
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parser.add_argument(
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'-o',
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'--optimizer',
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dest='optimizer',
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default='adam',
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help="Name of optimizer.")
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parser.add_argument(
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'-e', '--epoch', type=int, dest='epoch', default=200, help="No of epoch")
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parser.add_argument(
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'-i', '--iter', type=int, dest='iter', default=3, help="No of iter")
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parser.add_argument(
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'-p', '--patch', type=int, dest='patch', default=4, help="Length of patch")
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parser.add_argument(
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'-vs',
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'--valid_split',
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type=float,
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dest='valid_split',
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default=0.9,
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help="Percentage of validation split.")
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args = parser.parse_args()
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PARAM_DATASET = args.dataset # UP,IN,SV, KSC
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PARAM_EPOCH = args.epoch
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PARAM_ITER = args.iter
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PATCH_SIZE = args.patch
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PARAM_VAL = args.valid_split
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PARAM_OPTIM = args.optimizer
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# # Data Loading
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# for Monte Carlo runs
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seeds = [1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341]
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ensemble = 1
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global Dataset # UP,IN,SV, KSC
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dataset = PARAM_DATASET #input('Please input the name of Dataset(IN, UP, SV, KSC):')
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Dataset = dataset.upper()
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def load_dataset(Dataset, split=0.9):
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data_path = '../dataset/'
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if Dataset == 'IN':
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mat_data = sio.loadmat(data_path + 'Indian_pines_corrected.mat')
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mat_gt = sio.loadmat(data_path + 'Indian_pines_gt.mat')
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data_hsi = mat_data['indian_pines_corrected']
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gt_hsi = mat_gt['indian_pines_gt']
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K = 30
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TOTAL_SIZE = 10249
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VALIDATION_SPLIT = split
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TRAIN_SIZE = math.ceil(TOTAL_SIZE * VALIDATION_SPLIT)
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if Dataset == 'UP':
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uPavia = sio.loadmat(data_path + 'PaviaU.mat')
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gt_uPavia = sio.loadmat(data_path + 'PaviaU_gt.mat')
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data_hsi = uPavia['paviaU']
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gt_hsi = gt_uPavia['paviaU_gt']
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K = data_hsi.shape[2]
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TOTAL_SIZE = 42776
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VALIDATION_SPLIT = split
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TRAIN_SIZE = math.ceil(TOTAL_SIZE * VALIDATION_SPLIT)
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if Dataset == 'SV':
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SV = sio.loadmat(data_path + 'Salinas_corrected.mat')
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gt_SV = sio.loadmat(data_path + 'Salinas_gt.mat')
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data_hsi = SV['salinas_corrected']
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gt_hsi = gt_SV['salinas_gt']
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K = data_hsi.shape[2]
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TOTAL_SIZE = 54129
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VALIDATION_SPLIT = split
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TRAIN_SIZE = math.ceil(TOTAL_SIZE * VALIDATION_SPLIT)
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if Dataset == 'KSC':
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SV = sio.loadmat(data_path + 'KSC.mat')
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gt_SV = sio.loadmat(data_path + 'KSC_gt.mat')
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data_hsi = SV['KSC']
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gt_hsi = gt_SV['KSC_gt']
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K = data_hsi.shape[2]
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TOTAL_SIZE = 5211
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VALIDATION_SPLIT = split
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TRAIN_SIZE = math.ceil(TOTAL_SIZE * VALIDATION_SPLIT)
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shapeor = data_hsi.shape
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data_hsi = data_hsi.reshape(-1, data_hsi.shape[-1])
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data_hsi = PCA(n_components=K).fit_transform(data_hsi)
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shapeor = np.array(shapeor)
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