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Commit 35de0e38 authored by tomrink's avatar tomrink
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modules/deeplearning/icing_cnn.py
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......@@ -53,25 +53,24 @@ mean_std_dct.update(mean_std_dct_l1b)
mean_std_dct.update(mean_std_dct_l2)
# -- NIGHT L2 -----------------------------
# train_params_l2 = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
# 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_acha', 'cld_opd_acha']
# -- DAY L2 -------------
train_params_l2 = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
## 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp']
'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
train_params_l2_night = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_acha', 'cld_opd_acha']
# -- DAY L2 --------------------------------
train_params_l2_day = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
# -- DAY L1B --------------------------------
train_params_l1b = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
train_params_l1b_day = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
# -- NIGHT L1B -------------------------------
# train_params_l1b = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
# 'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom']
# -- DAY LUNAR ---------------------
train_params_l1b_night = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom']
# -- DAY LUNAR ---------------------------------
# train_params_l1b = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
# 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
# ---------------------------------------------
train_params = train_params_l2
train_params = train_params_l1b_day + train_params_l2_day
# -- Zero out params (Experimentation Only) ------------
zero_out_params = ['cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
DO_ZERO_OUT = False
......@@ -118,7 +117,17 @@ def build_residual_block(input, drop_rate, num_neurons, activation, block_name,
class IcingIntensityNN:
def __init__(self, gpu_device=0, datapath=None):
def __init__(self, day_night='DAY', gpu_device=0, datapath=None):
if day_night == 'DAY':
self.train_params_l1b = train_params_l1b_day
self.train_params_l2 = train_params_l2_day
self.train_params = train_params_l1b_day + train_params_l2_day
else:
self.train_params_l1b = train_params_l1b_night
self.train_params_l2 = train_params_l2_night
self.train_params = train_params_l1b_night + train_params_l2_night
self.train_data = None
self.train_label = None
self.test_data = None
......@@ -202,7 +211,7 @@ class IcingIntensityNN:
self.data_dct = None
n_chans = len(train_params)
n_chans = len(self.train_params)
if TRIPLET:
n_chans *= 3
self.X_img = tf.keras.Input(shape=(img_width, img_width, n_chans))
......@@ -252,7 +261,7 @@ class IcingIntensityNN:
nd_idxs = np.sort(nd_idxs)
data = []
for param in train_params:
for param in self.train_params:
nda = self.get_parameter_data(param, nd_idxs, is_training)
# Manual Corruption Process. Better: see use of tf.keras.layers.GaussianNoise
# if NOISE_TRAINING and is_training:
......@@ -295,12 +304,12 @@ class IcingIntensityNN:
def get_parameter_data(self, param, nd_idxs, is_training):
if is_training:
if param in train_params_l1b:
if param in self.train_params_l1b:
h5f = self.h5f_l1b_trn
else:
h5f = self.h5f_l2_trn
else:
if param in train_params_l1b:
if param in self.train_params_l1b:
h5f = self.h5f_l1b_tst
else:
h5f = self.h5f_l2_tst
......@@ -363,7 +372,7 @@ class IcingIntensityNN:
nd_idxs = np.sort(nd_idxs)
data = []
for param in train_params:
for param in self.train_params:
nda = self.data_dct[param][nd_idxs, ]
nda = normalize(nda, param, mean_std_dct)
data.append(nda)
......@@ -542,7 +551,7 @@ class IcingIntensityNN:
activation = tf.nn.leaky_relu
momentum = 0.99
num_filters = len(train_params) * 2
num_filters = len(self.train_params) * 2
if NOISE_TRAINING:
input_2d = tf.keras.layers.GaussianNoise(stddev=NOISE_STDDEV)(self.inputs[0])
......@@ -988,7 +997,7 @@ class IcingIntensityNN:
self.h5f_l2_tst.close()
def run_evaluate(self, filename, ckpt_dir):
data_dct, ll, cc = make_for_full_domain_predict(filename, name_list=train_params)
data_dct, ll, cc = make_for_full_domain_predict(filename, name_list=self.train_params)
self.setup_eval_pipeline(data_dct, len(ll))
self.build_model()
self.build_training()
......@@ -1081,7 +1090,7 @@ def run_evaluate_static(h5f, ckpt_dir_s_path, flight_level=4, prob_thresh=0.5, s
return ice_lons, ice_lats, preds_2d, lons_2d, lats_2d, x_rad, y_rad
def run_evaluate_static_new(data_dct, num_lines, num_elems, ckpt_dir_s_path, flight_levels=[0, 1, 2, 3, 4], prob_thresh=0.5):
def run_evaluate_static_new(data_dct, num_lines, num_elems, ckpt_dir_s_path, day_night='DAY', flight_levels=[0, 1, 2, 3, 4], prob_thresh=0.5):
ckpt_dir_s = os.listdir(ckpt_dir_s_path)
ckpt_dir = ckpt_dir_s[0]
......@@ -1090,7 +1099,7 @@ def run_evaluate_static_new(data_dct, num_lines, num_elems, ckpt_dir_s_path, fli
preds_2d_dct = {flvl: None for flvl in flight_levels}
for flvl in flight_levels:
nn = IcingIntensityNN()
nn = IcingIntensityNN(day_night=day_night)
nn.flight_level = flvl
nn.setup_eval_pipeline(data_dct, num_lines * num_elems)
nn.build_model()
......
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