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Commit 9bf1ddcd authored by tomrink's avatar tomrink
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......@@ -36,6 +36,7 @@ NOISE_TRAINING = False
NOISE_STDDEV = 0.01
DO_AUGMENT = True
DO_SMOOTH = False
DO_ZERO_OUT = False
DO_ESPCN = False # Note: If True, cannot do mixed resolution input fields (Adjust accordingly below)
......@@ -267,7 +268,6 @@ class SRCNN:
tmp = input_data[:, idx, :, :]
tmp = tmp.copy()
tmp = np.where(np.isnan(tmp), 0, tmp)
# tmp = smooth_2d(tmp, sigma=1.0)
if DO_ESPCN:
tmp = tmp[:, slc_y_2, slc_x_2]
else: # Half res upsampled to full res:
......@@ -282,7 +282,6 @@ class SRCNN:
tmp = input_data[:, idx, :, :]
tmp = tmp.copy()
tmp = np.where(np.isnan(tmp), 0, tmp)
# tmp = smooth_2d(tmp, sigma=1.0)
# Full res:
tmp = tmp[:, slc_y, slc_x]
tmp = normalize(tmp, param, mean_std_dct)
......@@ -293,7 +292,8 @@ class SRCNN:
tmp = input_data[:, label_idx, :, :]
tmp = tmp.copy()
tmp = np.where(np.isnan(tmp), 0, tmp)
# tmp = smooth_2d(tmp, sigma=1.0)
if DO_SMOOTH:
tmp = smooth_2d(tmp, sigma=0.5)
if DO_ESPCN:
tmp = tmp[:, slc_y_2, slc_x_2]
else: # Half res upsampled to full res:
......@@ -315,8 +315,9 @@ class SRCNN:
# -----------------------------------------------------
label = input_data[:, label_idx, :, :]
label = label.copy()
# label = np.where(np.isnan(label), 0, label)
# label = smooth_2d(label, sigma=1.0)
if DO_SMOOTH:
label = np.where(np.isnan(label), 0, label)
label = smooth_2d(label, sigma=0.5)
label = label[:, y_128, x_128]
if label_param != 'cloud_probability':
......
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