snapshot...

modules/deeplearning/srcnn_l1b_l2.py

 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
@@ -766,6 +768,82 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
         return out_sr, hr_grd_a, hr_grd_c
 
 
+def analyze(file='/Users/tomrink/cld_opd_out.npy'):
+    # Save this:
+    # nn.test_data_files = glob.glob('/Users/tomrink/data/clavrx_opd_valid_DAY/data_valid*.npy')
+    # idxs = np.arange(50)
+    # dat, lbl = nn.get_in_mem_data_batch(idxs, False)
+    # tmp = dat[:, 1:128, 1:128, 1]
+    # tmp = dat[:, 1:129, 1:129, 1]
+
+    tup = np.load(file, allow_pickle=True)
+    lbls = tup[0]
+    pred = tup[1]
+
+    lbls = lbls[:, :, :, 0]
+    pred = pred[:, :, :, 0]
+
+    diff = pred - lbls
+
+    mae = (np.sum(np.abs(diff))) / diff.size
+    print('MAE: ', mae)
+
+    bin_ranges = util.util.get_bin_ranges(0.0, 160.0, 20.0)
+
+    bin_edges = []
+    bin_ranges = []
+
+    bin_ranges.append([0.0, 5.0])
+    bin_edges.append(0.0)
+
+    bin_ranges.append([5.0, 10.0])
+    bin_edges.append(5.0)
+
+    bin_ranges.append([10.0, 15.0])
+    bin_edges.append(10.0)
+
+    bin_ranges.append([15.0, 20.0])
+    bin_edges.append(15.0)
+
+    bin_ranges.append([20.0, 30.0])
+    bin_edges.append(20.0)
+
+    bin_ranges.append([30.0, 40.0])
+    bin_edges.append(30.0)
+
+    bin_ranges.append([40.0, 60.0])
+    bin_edges.append(40.0)
+
+    bin_ranges.append([60.0, 80.0])
+    bin_edges.append(60.0)
+
+    bin_ranges.append([80.0, 100.0])
+    bin_edges.append(80.0)
+
+    bin_ranges.append([100.0, 120.0])
+    bin_edges.append(100.0)
+
+    bin_ranges.append([120.0, 140.0])
+    bin_edges.append(120.0)
+
+    bin_ranges.append([140.0, 160.0])
+    bin_edges.append(140.0)
+
+    bin_edges.append(160.0)
+
+    diff_by_value_bins = util.util.bin_data_by(diff.flatten(), lbls.flatten(), bin_ranges)
+
+    values = []
+    for k in range(len(bin_ranges)):
+        diff_k = diff_by_value_bins[k]
+        mae_k = (np.sum(np.abs(diff_k)) / diff_k.size)
+        values.append(int(mae_k/bin_ranges[k][1] * 100.0))
+
+        print('MAE: ', diff_k.size, bin_ranges[k], mae_k)
+
+    return np.array(values), bin_edges
+
+
 if __name__ == "__main__":
     nn = SRCNN()
     nn.run('matchup_filename')