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- import h5py
- import numpy as np
- import os
-
-
- def get_train_data(data_dir):
- with h5py.File(os.path.join(data_dir, './train_real.h5')) as f:
- label_real = f['train_real'][:]
- num, coil, ny, nx = label_real.shape
- #data_real = np.transpose(data_real, (0, 2, 1))
- with h5py.File(os.path.join(data_dir, './train_imag.h5')) as f:
- label_imag = f['train_imag'][:]
- #data_imag = np.transpose(kspace_imag, (0, 2, 1))
- label = label_real + 1j * label_imag
- label = np.transpose(label, (0, 3, 2, 1))
-
- num_train = 1900
- num_validate = 249
- train_label = label[0:num_train]
- validate_label = label[num_train:num_train + num_validate]
- return train_label, validate_label
-
- def get_test_data(data_dir):
- with h5py.File(os.path.join(data_dir, './test_real_45.h5')) as f:
- test_real = f['test_real'][:]
- num, nc, nt, ny, nx = test_real.shape
- #data_real = np.transpose(data_real, (0, 2, 1))
- with h5py.File(os.path.join(data_dir, './test_imag_45.h5')) as f:
- test_imag = f['test_imag'][:]
- #data_imag = np.transpose(kspace_imag, (0, 2, 1))
- test_label = test_real + 1j * test_imag
- test_label = np.transpose(test_label, (0, 4, 3, 2, 1))
-
- return test_label
-
- def get_fine_tuning_data(data_dir):
- with h5py.File(os.path.join(data_dir, './fine_tuning_real.h5')) as f:
- fine_tuning_real = f['fine_tuning_real'][:]
- num, nc, nt, ny, nx = fine_tuning_real.shape
- #data_real = np.transpose(data_real, (0, 2, 1))
- with h5py.File(os.path.join(data_dir, './fine_tuning_imag.h5')) as f:
- fine_tuning_imag = f['fine_tuning_imag'][:]
- #data_imag = np.transpose(kspace_imag, (0, 2, 1))
- fine_tuning_label = fine_tuning_real + 1j * fine_tuning_imag
- fine_tuning_label = np.transpose(fine_tuning_label, (0, 4, 3, 2, 1))
-
- return fine_tuning_label
-
- def get_train_data_UIH(data_dir):
- with h5py.File(os.path.join(data_dir, './UIH_Data.h5')) as f:
- UIH_real = f['trnData_real'][:]
- UIH_img = f['trnData_img'][:]
- mask_t = f['Mask_1D_x4'][:]
- UIH_data = UIH_real + 1j * UIH_img
- UIH_data = np.transpose(UIH_data, (0, 2, 3, 1))
-
- with h5py.File(os.path.join(data_dir, './trnData_CUBE.hdf5')) as f:
- GE_real = f['trnData_real'][:]
- GE_img = f['trnData_img'][:]
- GE_data = GE_real + 1j * GE_img
- GE_data = np.transpose(GE_data, (0, 2, 3, 1))
-
- kspace = np.concatenate((UIH_data, GE_data))
-
- # num_train = 500
- # num_validate = 110
- # train_kspace = kspace[0:num_train]
- # validate_kspace = kspace[num_train:num_train + num_validate]
- return kspace, mask_t
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