snapshot...

modules/deeplearning/cloud_fraction_fcn.py

@@ -717,7 +717,7 @@ class SRCNN:
 
         return labels, preds
 
-    def do_evaluate(self, data, ckpt_dir):
+    def do_evaluate(self, inputs, ckpt_dir):
 
         ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
         ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
@@ -725,7 +725,7 @@ class SRCNN:
 
         self.reset_test_metrics()
 
-        pred = self.model([data], training=False)
+        pred = self.model([inputs], training=False)
         self.test_probs = pred
         pred = pred.numpy()
 
@@ -756,7 +756,7 @@ class SRCNN:
         return self.restore(ckpt_dir)
 
     def run_evaluate(self, data, ckpt_dir):
-        #data = tf.convert_to_tensor(data, dtype=tf.float32)
+        # data = tf.convert_to_tensor(data, dtype=tf.float32)
         self.num_data_samples = 80000
         self.build_model()
         self.build_training()
@@ -813,10 +813,11 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
 
     nn = SRCNN()
     out_sr = nn.run_evaluate(data, ckpt_dir)
+    out_sr = out_sr.argmax(axis=3)
     if out_file is not None:
-        np.save(out_file, (out_sr[0, :, :, 0], grd_a, avg, grd_c))
+        np.save(out_file, (out_sr[0, :, :], grd_a, avg, grd_c))
     else:
-        return out_sr, grd_a, avg, grd_c
+        return out_sr[0, :, :], grd_a, avg, grd_c
 
 
 def analyze2(nda_m, nda_i):