diff --git a/modules/deeplearning/cloud_opd_srcnn_viirs.py b/modules/deeplearning/cloud_opd_srcnn_viirs.py
index 14b95ef119266ad1e0ff89b55f0238f02118a84a..e1f1df54bc09214ee4d10680923ca04dc4c15604 100644
--- a/modules/deeplearning/cloud_opd_srcnn_viirs.py
+++ b/modules/deeplearning/cloud_opd_srcnn_viirs.py
@@ -67,34 +67,6 @@ print('label_param: ', label_param)
KERNEL_SIZE = 3 # target size: (128, 128)
LEN_X = LEN_Y = 128
-if KERNEL_SIZE == 3:
- slc_x_m = slice(1, int(LEN_X/2) + 4)
- slc_y_m = slice(1, int(LEN_Y/2) + 4)
- slc_x = slice(3, LEN_X + 5)
- slc_y = slice(3, LEN_Y + 5)
- slc_x_2 = slice(2, LEN_X + 7, 2)
- slc_y_2 = slice(2, LEN_Y + 7, 2)
- x_2 = np.arange(int(LEN_X/2) + 3)
- y_2 = np.arange(int(LEN_Y/2) + 3)
- t = np.arange(0, int(LEN_X/2) + 3, 0.5)
- s = np.arange(0, int(LEN_Y/2) + 3, 0.5)
- x_k = slice(1, LEN_X + 3)
- y_k = slice(1, LEN_Y + 3)
- x_128 = slice(4, LEN_X + 4)
- y_128 = slice(4, LEN_Y + 4)
-elif KERNEL_SIZE == 5:
- slc_x = slice(3, 135)
- slc_y = slice(3, 135)
- slc_x_2 = slice(2, 137, 2)
- slc_y_2 = slice(2, 137, 2)
- x_128 = slice(5, 133)
- y_128 = slice(5, 133)
- t = np.arange(1, 67, 0.5)
- s = np.arange(1, 67, 0.5)
- x_2 = np.arange(68)
- y_2 = np.arange(68)
-# ----------------------------------------
-
def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.relu, padding='SAME',
kernel_initializer='he_uniform', scale=None, kernel_size=3,
@@ -119,12 +91,6 @@ def build_residual_conv2d_block(conv, num_filters, block_name, activation=tf.nn.
return conv
-def upsample(tmp):
- tmp = resample_2d_linear(x_2, y_2, tmp, t, s)
- tmp = tmp[:, y_k, x_k]
- return tmp
-
-
def upsample_nearest(grd):
bsize, ylen, xlen = grd.shape
up = np.zeros((bsize, ylen*2, xlen*2))
@@ -177,7 +143,7 @@ def get_min_max_std(grd_k):
class SRCNN:
- def __init__(self):
+ def __init__(self, LEN_Y=128, LEN_X=128):
self.train_data = None
self.train_label = None
@@ -190,20 +156,7 @@ class SRCNN:
self.test_dataset = None
self.eval_dataset = None
self.X_img = None
- self.X_prof = None
- self.X_u = None
- self.X_v = None
- self.X_sfc = None
self.inputs = []
- self.y = None
- self.handle = None
- self.inner_handle = None
- self.in_mem_batch = None
-
- self.h5f_l1b_trn = None
- self.h5f_l1b_tst = None
- self.h5f_l2_trn = None
- self.h5f_l2_tst = None
self.logits = None
@@ -225,8 +178,6 @@ class SRCNN:
self.writer_valid = None
self.writer_train_valid_loss = None
- self.OUT_OF_RANGE = False
-
self.model = None
self.optimizer = None
self.ema = None
@@ -234,14 +185,6 @@ class SRCNN:
self.train_accuracy = None
self.test_loss = None
self.test_accuracy = None
- self.test_auc = None
- self.test_recall = None
- self.test_precision = None
- self.test_confusion_matrix = None
- self.test_true_pos = None
- self.test_true_neg = None
- self.test_false_pos = None
- self.test_false_neg = None
self.test_labels = []
self.test_preds = []
@@ -251,7 +194,6 @@ class SRCNN:
self.num_data_samples = None
self.initial_learning_rate = None
- self.data_dct = None
self.train_data_files = None
self.train_label_files = None
self.test_data_files = None
@@ -264,8 +206,28 @@ class SRCNN:
self.inputs.append(self.X_img)
+ self.slc_x_m = slice(1, int(LEN_X / 2) + 4)
+ self.slc_y_m = slice(1, int(LEN_Y / 2) + 4)
+ self.slc_x = slice(3, LEN_X + 5)
+ self.slc_y = slice(3, LEN_Y + 5)
+ self.slc_x_2 = slice(2, LEN_X + 7, 2)
+ self.slc_y_2 = slice(2, LEN_Y + 7, 2)
+ self.x_2 = np.arange(int(LEN_X / 2) + 3)
+ self.y_2 = np.arange(int(LEN_Y / 2) + 3)
+ self.t = np.arange(0, int(LEN_X / 2) + 3, 0.5)
+ self.s = np.arange(0, int(LEN_Y / 2) + 3, 0.5)
+ self.x_k = slice(1, LEN_X + 3)
+ self.y_k = slice(1, LEN_Y + 3)
+ self.x_128 = slice(4, LEN_X + 4)
+ self.y_128 = slice(4, LEN_Y + 4)
+
tf.debugging.set_log_device_placement(LOG_DEVICE_PLACEMENT)
+ def upsample(self, grd):
+ grd = resample_2d_linear(self.x_2, self.y_2, grd, self.t, self.s)
+ grd = grd[:, self.y_k, self.x_k]
+ return grd
+
def get_in_mem_data_batch(self, idxs, is_training):
if is_training:
data_files = self.train_data_files
@@ -292,8 +254,8 @@ class SRCNN:
idx = params.index(param)
tmp = input_data[:, idx, :, :]
tmp = np.where(np.isnan(tmp), 0, tmp)
- tmp = tmp[:, slc_y_m, slc_x_m]
- tmp = upsample(tmp)
+ tmp = tmp[:, self.slc_y_m, self.slc_x_m]
+ tmp = self.upsample(tmp)
tmp = normalize(tmp, param, mean_std_dct)
data_norm.append(tmp)
@@ -310,17 +272,17 @@ class SRCNN:
# hi = normalize(hi, param, mean_std_dct)
# avg = normalize(avg, param, mean_std_dct)
#
- # data_norm.append(lo[:, slc_y, slc_x])
- # data_norm.append(hi[:, slc_y, slc_x])
- # data_norm.append(avg[:, slc_y, slc_x])
+ # data_norm.append(lo[:, self.slc_y, self.slc_x])
+ # data_norm.append(hi[:, self.slc_y, self.slc_x])
+ # data_norm.append(avg[:, self.slc_y, self.slc_x])
tmp = normalize(tmp, param, mean_std_dct)
- data_norm.append(tmp[:, slc_y, slc_x])
+ data_norm.append(tmp[:, self.slc_y, self.slc_x])
# ---------------------------------------------------
tmp = input_label[:, label_idx_i, :, :]
tmp = np.where(np.isnan(tmp), 0, tmp)
- tmp = tmp[:, slc_y_2, slc_x_2]
- tmp = upsample(tmp)
+ tmp = tmp[:, self.slc_y_2, self.slc_x_2]
+ tmp = self.upsample(tmp)
tmp = normalize(tmp, label_param, mean_std_dct)
data_norm.append(tmp)
# ---------
@@ -331,7 +293,7 @@ class SRCNN:
# -----------------------------------------------------
label = input_label[:, label_idx_i, :, :]
label = normalize(label, label_param, mean_std_dct)
- label = label[:, y_128, x_128]
+ label = label[:, self.y_128, self.x_128]
label = np.where(np.isnan(label), 0, label)
label = np.expand_dims(label, axis=3)
@@ -535,19 +497,6 @@ class SRCNN:
self.test_loss.reset_states()
self.test_accuracy.reset_states()
- def get_metrics(self):
- recall = self.test_recall.result()
- precsn = self.test_precision.result()
- f1 = 2 * (precsn * recall) / (precsn + recall)
-
- tn = self.test_true_neg.result()
- tp = self.test_true_pos.result()
- fn = self.test_false_neg.result()
- fp = self.test_false_pos.result()
-
- mcc = ((tp * tn) - (fp * fn)) / np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
- return f1, mcc
-
def do_training(self, ckpt_dir=None):
if ckpt_dir is None:
@@ -671,10 +620,10 @@ class SRCNN:
preds = np.concatenate(self.test_preds)
print(labels.shape, preds.shape)
- labels = denormalize(labels, label_param, mean_std_dct)
- preds = denormalize(preds, label_param, mean_std_dct)
+ labels_denorm = denormalize(labels, label_param, mean_std_dct)
+ preds_denorm = denormalize(preds, label_param, mean_std_dct)
- return labels, preds
+ return labels_denorm, preds_denorm
def do_evaluate(self, inputs, ckpt_dir):