snapshot...

modules/deeplearning/cloud_fraction_fcn.py

@@ -2,19 +2,12 @@ import glob
 import tensorflow as tf
 
 import util.util
-from util.setup import logdir, modeldir, cachepath, now, ancillary_path
-from util.util import EarlyStop, normalize, denormalize, resample, resample_2d_linear, resample_one,\
-    resample_2d_linear_one, get_grid_values_all, add_noise, smooth_2d, smooth_2d_single, median_filter_2d,\
-    median_filter_2d_single, downscale_2x
+from util.setup import logdir, modeldir, now, ancillary_path
+from util.util import EarlyStop, normalize, get_grid_values_all
 import os, datetime
 import numpy as np
 import pickle
 import h5py
-from scipy.ndimage import gaussian_filter
-
-# L1B M/I-bands: /apollo/cloud/scratch/cwhite/VIIRS_HRES/2019/2019_01_01/
-# CLAVRx: /apollo/cloud/scratch/Satellite_Output/VIIRS_HRES/2019/2019_01_01/
-# /apollo/cloud/scratch/Satellite_Output/andi/NEW/VIIRS_HRES/2019
 
 LOG_DEVICE_PLACEMENT = False
 
@@ -326,7 +319,6 @@ class SRCNN:
             tmp = tmp.copy()
 
             lo, hi, std, avg = get_min_max_std(tmp)
-            # std = np.where(np.isnan(std), 0, std)
             lo = normalize(lo, param, mean_std_dct)
             hi = normalize(hi, param, mean_std_dct)
             avg = normalize(avg, param, mean_std_dct)
@@ -334,7 +326,6 @@ class SRCNN:
             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(std[:, slc_y, slc_x])
         # ---------------------------------------------------
         tmp = input_data[:, label_idx, :, :]
         tmp = tmp.copy()
@@ -776,36 +767,37 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
 
     h5f = h5py.File(in_file, 'r')
 
-    grd_a = get_grid_values_all(h5f, 'orig/temp_11_0um')
-    grd_a = np.where(np.isnan(grd_a), 0, grd_a)
-    grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
-
-    grd_b = get_grid_values_all(h5f, 'super/refl_0_65um')
-    grd_b = np.where(np.isnan(grd_b), 0, grd_b)
-    grd_b = np.expand_dims(grd_b, axis=0)
-    lo, hi, std, avg = get_min_max_std(grd_b)
-    lo = normalize(lo, 'refl_0_65um_nom', mean_std_dct)
-    hi = normalize(hi, 'refl_0_65um_nom', mean_std_dct)
-    avg = normalize(avg, 'refl_0_65um_nom', mean_std_dct)
-    lo = np.squeeze(lo)
-    hi = np.squeeze(hi)
-    avg = np.squeeze(avg)
-
-    grd_c = get_grid_values_all(h5f, 'orig/'+label_param)
-    grd_c = np.where(np.isnan(grd_c), 0, grd_c)
-
-    data = np.stack([grd_a, lo, hi, avg, grd_c], axis=2)
+    bt = get_grid_values_all(h5f, 'orig/temp_11_0um')
+    bt = np.where(np.isnan(bt), 0, bt)
+    bt = normalize(bt, 'temp_11_0um_nom', mean_std_dct)
+
+    refl = get_grid_values_all(h5f, 'super/refl_0_65um')
+    refl = np.where(np.isnan(refl), 0, refl)
+    refl = np.expand_dims(refl, axis=0)
+    refl_lo, refl_hi, refl_std, refl_avg = get_min_max_std(refl)
+    refl_lo = normalize(refl_lo, 'refl_0_65um_nom', mean_std_dct)
+    refl_hi = normalize(refl_hi, 'refl_0_65um_nom', mean_std_dct)
+    refl_avg = normalize(refl_avg, 'refl_0_65um_nom', mean_std_dct)
+    refl_lo = np.squeeze(refl_lo)
+    refl_hi = np.squeeze(refl_hi)
+    refl_avg = np.squeeze(refl_avg)
+
+    cp = get_grid_values_all(h5f, 'orig/'+label_param)
+    cp = np.where(np.isnan(cp), 0, cp)
+
+    data = np.stack([bt, refl_lo, refl_hi, refl_avg, cp], axis=2)
     data = np.expand_dims(data, axis=0)
 
     h5f.close()
 
     nn = SRCNN()
-    out_sr = nn.run_evaluate(data, ckpt_dir)
-    out_sr = out_sr.argmax(axis=3)
+    probs = nn.run_evaluate(data, ckpt_dir)
+    cld_frac = probs.argmax(axis=3)
+
     if out_file is not None:
-        np.save(out_file, (out_sr[0, :, :], grd_a, avg, grd_c))
+        np.save(out_file, (cld_frac[0, :, :], bt, refl_avg, cp))
     else:
-        return out_sr[0, :, :], grd_a, avg, grd_c
+        return cld_frac[0, :, :], bt, refl_avg, cp
 
 
 def analyze2(nda_m, nda_i):