diff --git a/modules/deeplearning/unet_l1b_l2.py b/modules/deeplearning/unet_l1b_l2.py
index 42cc1e8bbc22a8f12dc7093c24a45226349c91c1..a8a05b0b6ce1ef97e79ef2177f93520142d49a7f 100644
--- a/modules/deeplearning/unet_l1b_l2.py
+++ b/modules/deeplearning/unet_l1b_l2.py
@@ -37,11 +37,24 @@ DO_AUGMENT = True
img_width = 16
-mean_std_file = home_dir+'/viirs_emis_rad_mean_std.pkl'
+# setup scaling parameters dictionary
+mean_std_dct = {}
+mean_std_file = ancillary_path+'mean_std_lo_hi_l2.pkl'
f = open(mean_std_file, 'rb')
-mean_std_dct = pickle.load(f)
+mean_std_dct_l2 = pickle.load(f)
f.close()
+mean_std_file = ancillary_path+'mean_std_lo_hi_l1b.pkl'
+f = open(mean_std_file, 'rb')
+mean_std_dct_l1b = pickle.load(f)
+f.close()
+
+mean_std_dct.update(mean_std_dct_l1b)
+mean_std_dct.update(mean_std_dct_l2)
+
+emis_params = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_9um_nom',
+ 'temp_6_7um_nom']
+
# -- Zero out params (Experimentation Only) ------------
zero_out_params = ['cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
DO_ZERO_OUT = False
@@ -164,14 +177,14 @@ class UNET:
self.test_label_nda = None
# self.n_chans = len(self.train_params)
- self.n_chans = 1
+ self.n_chans = 6
if TRIPLET:
self.n_chans *= 3
self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
self.inputs.append(self.X_img)
# self.inputs.append(tf.keras.Input(shape=(None, None, 5)))
- self.inputs.append(tf.keras.Input(shape=(None, None, 1)))
+ self.inputs.append(tf.keras.Input(shape=(None, None, 6)))
self.flight_level = 0
@@ -198,56 +211,6 @@ class UNET:
# # Memory growth must be set before GPUs have been initialized
# print(e)
- # def get_in_mem_data_batch(self, idxs, is_training):
- #
- # # sort these to use as numpy indexing arrays
- # nd_idxs = np.array(idxs)
- # nd_idxs = np.sort(nd_idxs)
- #
- # data = []
- # for param in self.train_params:
- # nda = self.get_parameter_data(param, nd_idxs, is_training)
- # nda = normalize(nda, param, mean_std_dct)
- # if DO_ZERO_OUT and is_training:
- # try:
- # zero_out_params.index(param)
- # nda[:,] = 0.0
- # except ValueError:
- # pass
- # data.append(nda)
- # data = np.stack(data)
- # data = data.astype(np.float32)
- # data = np.transpose(data, axes=(1, 2, 3, 0))
- #
- # data_alt = self.get_scalar_data(nd_idxs, is_training)
- #
- # label = self.get_label_data(nd_idxs, is_training)
- # label = np.where(label == -1, 0, label)
- #
- # # binary, two class
- # if NumClasses == 2:
- # label = np.where(label != 0, 1, label)
- # label = label.reshape((label.shape[0], 1))
- # elif NumClasses == 3:
- # label = np.where(np.logical_or(label == 1, label == 2), 1, label)
- # label = np.where(np.invert(np.logical_or(label == 0, label == 1)), 2, label)
- # label = label.reshape((label.shape[0], 1))
- #
- # if is_training and DO_AUGMENT:
- # data_ud = np.flip(data, axis=1)
- # data_alt_ud = np.copy(data_alt)
- # label_ud = np.copy(label)
- #
- # data_lr = np.flip(data, axis=2)
- # data_alt_lr = np.copy(data_alt)
- # label_lr = np.copy(label)
- #
- # data = np.concatenate([data, data_ud, data_lr])
- # data_alt = np.concatenate([data_alt, data_alt_ud, data_alt_lr])
- # label = np.concatenate([label, label_ud, label_lr])
- #
- # return data, data_alt, label
-
def get_in_mem_data_batch(self, idxs, is_training):
if is_training:
train_data = []
@@ -259,10 +222,10 @@ class UNET:
f = self.train_label_files[k]
nda = np.load(f)
- train_label.append(nda)
+ train_label.append(nda[:, 0, :, :])
data = np.concatenate(train_data)
- data = np.expand_dims(data, axis=3)
+
label = np.concatenate(train_label)
label = np.expand_dims(label, axis=3)
else:
@@ -275,10 +238,9 @@ class UNET:
f = self.test_label_files[k]
nda = np.load(f)
- test_label.append(nda)
+ test_label.append(nda[:, 0, :, :])
data = np.concatenate(test_data)
- data = np.expand_dims(data, axis=3)
label = np.concatenate(test_label)
label = np.expand_dims(label, axis=3)
@@ -286,8 +248,13 @@ class UNET:
data = data.astype(np.float32)
label = label.astype(np.float32)
- data = normalize(data, 'M15', mean_std_dct)
- label = normalize(label, 'M15', mean_std_dct)
+ data_norm = []
+ for idx, param in enumerate(emis_params):
+ tmp = normalize(data[:, idx, :, :], param, mean_std_dct)
+ data_norm.append(tmp)
+ data = np.stack(data_norm, axis=3)
+
+ # label = normalize(label, 'M15', mean_std_dct)
if is_training and DO_AUGMENT:
data_ud = np.flip(data, axis=1)
@@ -301,38 +268,6 @@ class UNET:
return data, data, label
- # def get_parameter_data(self, param, nd_idxs, is_training):
- # if is_training:
- # if param in self.train_params_l1b:
- # h5f = self.h5f_l1b_trn
- # else:
- # h5f = self.h5f_l2_trn
- # else:
- # if param in self.train_params_l1b:
- # h5f = self.h5f_l1b_tst
- # else:
- # h5f = self.h5f_l2_tst
- #
- # nda = h5f[param][nd_idxs,]
- # return nda
- #
- # def get_label_data(self, nd_idxs, is_training):
- # # Note: labels will be same for nd_idxs across both L1B and L2
- # if is_training:
- # if self.h5f_l1b_trn is not None:
- # h5f = self.h5f_l1b_trn
- # else:
- # h5f = self.h5f_l2_trn
- # else:
- # if self.h5f_l1b_tst is not None:
- # h5f = self.h5f_l1b_tst
- # else:
- # h5f = self.h5f_l2_tst
- #
- # label = h5f['icing_intensity'][nd_idxs]
- # label = label.astype(np.int32)
- # return label
-
def get_in_mem_data_batch_train(self, idxs):
return self.get_in_mem_data_batch(idxs, True)
@@ -402,55 +337,6 @@ class UNET:
dataset = dataset.map(self.data_function_evaluate, num_parallel_calls=8)
self.eval_dataset = dataset
- # def setup_pipeline(self, filename_l1b_trn, filename_l1b_tst, filename_l2_trn, filename_l2_tst, trn_idxs=None, tst_idxs=None, seed=None):
- # if filename_l1b_trn is not None:
- # self.h5f_l1b_trn = h5py.File(filename_l1b_trn, 'r')
- # if filename_l1b_tst is not None:
- # self.h5f_l1b_tst = h5py.File(filename_l1b_tst, 'r')
- # if filename_l2_trn is not None:
- # self.h5f_l2_trn = h5py.File(filename_l2_trn, 'r')
- # if filename_l2_tst is not None:
- # self.h5f_l2_tst = h5py.File(filename_l2_tst, 'r')
- #
- # if trn_idxs is None:
- # # Note: time is same across both L1B and L2 for idxs
- # if self.h5f_l1b_trn is not None:
- # h5f = self.h5f_l1b_trn
- # else:
- # h5f = self.h5f_l2_trn
- # time = h5f['time']
- # trn_idxs = np.arange(time.shape[0])
- # if seed is not None:
- # np.random.seed(seed)
- # np.random.shuffle(trn_idxs)
- #
- # if self.h5f_l1b_tst is not None:
- # h5f = self.h5f_l1b_tst
- # else:
- # h5f = self.h5f_l2_tst
- # time = h5f['time']
- # tst_idxs = np.arange(time.shape[0])
- # if seed is not None:
- # np.random.seed(seed)
- # np.random.shuffle(tst_idxs)
- #
- # self.num_data_samples = trn_idxs.shape[0]
- #
- # self.get_train_dataset(trn_idxs)
- # self.get_test_dataset(tst_idxs)
- #
- # print('datetime: ', now)
- # print('training and test data: ')
- # print(filename_l1b_trn)
- # print(filename_l1b_tst)
- # print(filename_l2_trn)
- # print(filename_l2_tst)
- # print('---------------------------')
- # print('num train samples: ', self.num_data_samples)
- # print('BATCH SIZE: ', BATCH_SIZE)
- # print('num test samples: ', tst_idxs.shape[0])
- # print('setup_pipeline: Done')
-
def setup_pipeline(self, data_nda, label_nda, perc=0.20):
num_samples = data_nda.shape[0]
@@ -484,6 +370,9 @@ class UNET:
num_test_files = int(num_files * perc)
num_train_files = num_files - num_test_files
+ num_test_files = 1
+ num_train_files = 3
+
self.train_data_files = data_files[0:num_train_files]
self.train_label_files = label_files[0:num_train_files]
self.test_data_files = data_files[num_train_files:]
@@ -496,7 +385,7 @@ class UNET:
self.get_train_dataset(trn_idxs)
self.get_test_dataset(tst_idxs)
- self.num_data_samples = num_train_files * 30 # approximately
+ self.num_data_samples = num_train_files * 1000 # approximately
print('datetime: ', now)
print('training and test data: ')
@@ -1007,8 +896,8 @@ class UNET:
self.do_training()
def run_test(self, directory):
- data_files = glob.glob(directory+'mod_res*.npy')
- label_files = [f.replace('mod', 'img') for f in data_files]
+ data_files = glob.glob(directory+'l1b_*.npy')
+ label_files = [f.replace('l1b', 'l2') for f in data_files]
self.setup_pipeline_files(data_files, label_files)
self.build_model()
self.build_training()