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Commit a982b2b3 authored by tomrink's avatar tomrink
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...@@ -56,7 +56,8 @@ label_param = 'cld_opd_dcomp' ...@@ -56,7 +56,8 @@ label_param = 'cld_opd_dcomp'
params = ['temp_11_0um_nom', 'refl_0_65um_nom', 'refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01', 'temp_stddev3x3_ch31', 'refl_stddev3x3_ch01', label_param] params = ['temp_11_0um_nom', 'refl_0_65um_nom', 'refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01', 'temp_stddev3x3_ch31', 'refl_stddev3x3_ch01', label_param]
params_i = ['temp_11_0um_nom', 'refl_0_65um_nom', 'temp_stddev3x3_ch31', 'refl_stddev3x3_ch01', label_param] params_i = ['temp_11_0um_nom', 'refl_0_65um_nom', 'temp_stddev3x3_ch31', 'refl_stddev3x3_ch01', label_param]
data_params_half = ['temp_11_0um_nom', 'refl_0_65um_nom'] # data_params_half = ['temp_11_0um_nom', 'refl_0_65um_nom']
data_params_half = ['temp_11_0um_nom']
data_params_full = ['refl_0_65um_nom'] data_params_full = ['refl_0_65um_nom']
sub_fields = ['refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01'] sub_fields = ['refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01']
# sub_fields = ['refl_stddev3x3_ch01'] # sub_fields = ['refl_stddev3x3_ch01']
...@@ -209,7 +210,7 @@ class SRCNN: ...@@ -209,7 +210,7 @@ class SRCNN:
self.test_label_files = None self.test_label_files = None
# self.n_chans = len(data_params_half) + len(data_params_full) + 1 # self.n_chans = len(data_params_half) + len(data_params_full) + 1
self.n_chans = 6 self.n_chans = 3
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans)) self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
...@@ -269,6 +270,11 @@ class SRCNN: ...@@ -269,6 +270,11 @@ class SRCNN:
tmp = normalize(tmp, param, mean_std_dct) tmp = normalize(tmp, param, mean_std_dct)
data_norm.append(tmp) data_norm.append(tmp)
tmp = input_label[:, label_idx_i, :, :]
tmp = np.where(np.isnan(tmp), 0, tmp)
tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
data_norm.append(tmp[:, self.slc_y, self.slc_x])
tmp = input_label[:, label_idx_i, :, :] tmp = input_label[:, label_idx_i, :, :]
tmp = tmp.copy() tmp = tmp.copy()
tmp = np.where(np.isnan(tmp), 0.0, tmp) tmp = np.where(np.isnan(tmp), 0.0, tmp)
...@@ -291,16 +297,16 @@ class SRCNN: ...@@ -291,16 +297,16 @@ class SRCNN:
# tmp = np.where(np.isnan(tmp), 0.0, tmp) # tmp = np.where(np.isnan(tmp), 0.0, tmp)
# data_norm.append(tmp) # data_norm.append(tmp)
for param in sub_fields: # for param in sub_fields:
idx = params.index(param) # idx = params.index(param)
tmp = input_data[:, idx, :, :] # tmp = input_data[:, idx, :, :]
tmp = upsample_nearest(tmp) # tmp = upsample_nearest(tmp)
tmp = tmp[:, self.slc_y, self.slc_x] # tmp = tmp[:, self.slc_y, self.slc_x]
if param != 'refl_substddev_ch01': # if param != 'refl_substddev_ch01':
tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct) # tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
else: # else:
tmp = np.where(np.isnan(tmp), 0, tmp) # tmp = np.where(np.isnan(tmp), 0, tmp)
data_norm.append(tmp) # data_norm.append(tmp)
# --------------------------------------------------- # ---------------------------------------------------
data = np.stack(data_norm, axis=3) data = np.stack(data_norm, axis=3)
...@@ -409,7 +415,7 @@ class SRCNN: ...@@ -409,7 +415,7 @@ class SRCNN:
activation = tf.nn.relu activation = tf.nn.relu
momentum = 0.99 momentum = 0.99
num_filters = 64 num_filters = 32
input_2d = self.inputs[0] input_2d = self.inputs[0]
print('input: ', input_2d.shape) print('input: ', input_2d.shape)
......
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