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Commit f840b10d authored by tomrink's avatar tomrink
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......@@ -69,8 +69,8 @@ KERNEL_SIZE = 3 # target size: (128, 128)
N_X = N_Y = 1
if KERNEL_SIZE == 3:
slc_x = slice(2, N_X*128 + 4)
slc_y = slice(2, N_Y*128 + 4)
slc_x = slice(3, N_X*128 + 5)
slc_y = slice(3, N_Y*128 + 5)
slc_x_2 = slice(1, int((N_X*128)/2) + 4)
slc_y_2 = slice(1, int((N_Y*128)/2) + 4)
x_2 = np.arange(int((N_X*128)/2) + 3)
......@@ -79,8 +79,8 @@ if KERNEL_SIZE == 3:
s = np.arange(0, int((N_Y*128)/2) + 3, 0.5)
x_k = slice(1, N_X*128 + 3)
y_k = slice(1, N_Y*128 + 3)
x_128 = slice(3, N_X*128 + 3)
y_128 = slice(3, N_Y*128 + 3)
x_128 = slice(4, N_X*128 + 4)
y_128 = slice(4, N_Y*128 + 4)
elif KERNEL_SIZE == 5:
slc_x = slice(3, 135)
slc_y = slice(3, 135)
......@@ -301,17 +301,20 @@ class SRCNN:
tmp = input_label[:, idx, :, :]
tmp = np.where(np.isnan(tmp), 0, tmp)
lo, hi, std, avg = get_min_max_std(tmp)
lo = upsample_nearest(lo)
hi = upsample_nearest(hi)
avg = upsample_nearest(avg)
lo = normalize(lo, param, mean_std_dct)
hi = normalize(hi, param, mean_std_dct)
avg = normalize(avg, param, mean_std_dct)
data_norm.append(lo[:, slc_y, slc_x])
data_norm.append(hi[:, slc_y, slc_x])
data_norm.append(avg[:, slc_y, slc_x])
# lo, hi, std, avg = get_min_max_std(tmp)
# lo = upsample_nearest(lo)
# hi = upsample_nearest(hi)
# avg = upsample_nearest(avg)
# lo = normalize(lo, param, mean_std_dct)
# hi = normalize(hi, param, mean_std_dct)
# avg = normalize(avg, param, mean_std_dct)
#
# data_norm.append(lo[:, slc_y, slc_x])
# data_norm.append(hi[:, slc_y, slc_x])
# data_norm.append(avg[:, slc_y, slc_x])
tmp = normalize(tmp, param, mean_std_dct)
data_norm.append(tmp[:, slc_y, slc_x])
# ---------------------------------------------------
tmp = input_data[:, label_idx, :, :]
tmp = np.where(np.isnan(tmp), 0, tmp)
......
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