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Commit 66428b45 authored by tomrink's avatar tomrink
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...@@ -213,7 +213,7 @@ class SRCNN: ...@@ -213,7 +213,7 @@ class SRCNN:
self.test_label_nda = None self.test_label_nda = None
# self.n_chans = len(data_params) + 2 # self.n_chans = len(data_params) + 2
self.n_chans = 2 self.n_chans = 1
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans)) self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
# self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans)) # self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
...@@ -246,18 +246,18 @@ class SRCNN: ...@@ -246,18 +246,18 @@ class SRCNN:
DO_ADD_NOISE = True DO_ADD_NOISE = True
data_norm = [] data_norm = []
for param in data_params: # for param in data_params:
idx = params.index(param) # idx = params.index(param)
tmp = input_data[:, idx, :, :] # tmp = input_data[:, idx, :, :]
tmp = np.where(np.isnan(tmp), 0, tmp) # tmp = np.where(np.isnan(tmp), 0, tmp)
tmp = smooth_2d(tmp, sigma=1.0) # tmp = smooth_2d(tmp, sigma=1.0)
tmp = tmp[:, slc_y_2, slc_x_2] # tmp = tmp[:, slc_y_2, slc_x_2]
tmp = resample_2d_linear(x_2, y_2, tmp, t, s) # tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
tmp = tmp[:, y_k, x_k] # tmp = tmp[:, y_k, x_k]
tmp = normalize(tmp, param, mean_std_dct) # tmp = normalize(tmp, param, mean_std_dct)
if DO_ADD_NOISE: # if DO_ADD_NOISE:
tmp = add_noise(tmp, noise_scale=NOISE_STDDEV) # tmp = add_noise(tmp, noise_scale=NOISE_STDDEV)
data_norm.append(tmp) # data_norm.append(tmp)
# # -------------------------- # # --------------------------
# param = 'refl_0_65um_nom' # param = 'refl_0_65um_nom'
# idx = params.index(param) # idx = params.index(param)
...@@ -777,7 +777,8 @@ def run_evaluate_static(in_file, out_file, ckpt_dir): ...@@ -777,7 +777,8 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
grd_c = normalize(grd_c, label_param, mean_std_dct) grd_c = normalize(grd_c, label_param, mean_std_dct)
# data = np.stack([grd_a, grd_b, grd_c], axis=2) # data = np.stack([grd_a, grd_b, grd_c], axis=2)
data = np.stack([grd_a, grd_c], axis=2) #data = np.stack([grd_a, grd_c], axis=2)
data = np.stack([grd_c], axis=2)
data = np.expand_dims(data, axis=0) data = np.expand_dims(data, axis=0)
nn = SRCNN() nn = SRCNN()
......
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