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907 | 907 |
|
908 | 908 | epochs_path = f'./trained_models/S5Net/GSR-S5Net-st/{im_tag}/'
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909 | 909 |
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910 |
| -img_SR_S5Net = np.zeros([img_GT.shape[0],img_GT.shape[1],img_GT.shape[2]]) |
| 910 | +img_SR_GSR_S5Net_st = np.zeros([img_GT.shape[0],img_GT.shape[1],img_GT.shape[2]]) |
911 | 911 |
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912 | 912 | tc = 1
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913 | 913 | ker = kernel(tc,ratio,'cubic')
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|
1016 | 1016 |
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1017 | 1017 | epochs_path = f'./trained_models/S5Net/GSR-S5Net-dyn/{im_tag}/'
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1018 | 1018 |
|
1019 |
| -img_SR_S5Net = np.zeros([img_GT.shape[0],img_GT.shape[1],img_GT.shape[2]]) |
| 1019 | +img_SR_GSR_S5Net_dyn = np.zeros([img_GT.shape[0],img_GT.shape[1],img_GT.shape[2]]) |
1020 | 1020 |
|
1021 | 1021 | tc = 1
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1022 | 1022 | ker = kernel(tc,ratio,'cubic')
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1125 | 1125 |
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1126 | 1126 | epochs_path = f'./trained_models/S5Net/DSR-S5Net-st/{im_tag}/'
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1127 | 1127 |
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1128 |
| -img_SR_S5Net = np.zeros([img_GT.shape[0],img_GT.shape[1],img_GT.shape[2]]) |
| 1128 | +img_SR_DSR_S5Net_st = np.zeros([img_GT.shape[0],img_GT.shape[1],img_GT.shape[2]]) |
1129 | 1129 |
|
1130 | 1130 | tc = 1
|
1131 | 1131 | ker = kernel(tc,ratio,'cubic')
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1234 | 1234 |
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1235 | 1235 | epochs_path = f'./trained_models/S5Net/DSR-S5Net-dyn/{im_tag}/'
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1236 | 1236 |
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1237 |
| -img_SR_S5Net = np.zeros([img_GT.shape[0],img_GT.shape[1],img_GT.shape[2]]) |
| 1237 | +img_SR_DSR_S5Net_dyn = np.zeros([img_GT.shape[0],img_GT.shape[1],img_GT.shape[2]]) |
1238 | 1238 |
|
1239 | 1239 | tc = 1
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1240 | 1240 | ker = kernel(tc,ratio,'cubic')
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