snapshot...

modules/deeplearning/icing_fcn.py

@@ -410,21 +410,18 @@ class IcingIntensityFCN:
         return self.get_in_mem_data_batch(idxs, False)
 
     def get_in_mem_data_batch_eval(self, idxs):
-        # 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.data_dct[param][nd_idxs, ]
+            nda = self.data_dct[param]
             nda = normalize(nda, param, mean_std_dct)
             data.append(nda)
         data = np.stack(data)
         data = data.astype(np.float32)
-        data = np.transpose(data, axes=(1, 2, 3, 0))
+        data = np.transpose(data, axes=(1, 2, 0))
+        data = np.expand_dims(data, axis=0)
 
         # TODO: altitude data will be specified by user at run-time
-        nda = np.zeros([nd_idxs.size])
+        nda = np.zeros([data.shape[1]*data.shape[2]])
         nda[:] = self.flight_level
         nda = tf.one_hot(nda, 5).numpy()
 
@@ -470,9 +467,9 @@ class IcingIntensityFCN:
         indexes = list(indexes)
 
         dataset = tf.data.Dataset.from_tensor_slices(indexes)
-        dataset = dataset.batch(PROC_BATCH_SIZE)
+        # dataset = dataset.batch(PROC_BATCH_SIZE)
         dataset = dataset.map(self.data_function_evaluate, num_parallel_calls=8)
-        dataset = dataset.cache()
+        # dataset = dataset.cache()
         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):
@@ -548,9 +545,9 @@ class IcingIntensityFCN:
         print('num test samples: ', tst_idxs.shape[0])
         print('setup_test_pipeline: Done')
 
-    def setup_eval_pipeline(self, data_dct, num_tiles):
+    def setup_eval_pipeline(self, data_dct):
         self.data_dct = data_dct
-        idxs = np.arange(num_tiles)
+        idxs = np.arange(1)
         self.num_data_samples = idxs.shape[0]
 
         self.get_evaluate_dataset(idxs)
@@ -973,22 +970,21 @@ class IcingIntensityFCN:
 
         self.test_preds = preds
 
-    def do_evaluate(self, ckpt_dir=None, prob_thresh=0.5):
-
-        # if ckpt_dir is not None:  # if is None, this has been done already
-        #     ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
-        #     ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
-        #     ckpt.restore(ckpt_manager.latest_checkpoint)
+    def do_evaluate(self, prob_thresh=0.5):
 
         self.reset_test_metrics()
 
         pred_s = []
+        # for data in self.eval_dataset:
+        #     ds = tf.data.Dataset.from_tensor_slices(data)
+        #     ds = ds.batch(BATCH_SIZE)
+        #     for mini_batch in ds:
+        #         pred = self.model([mini_batch], training=False)
+        #         pred_s.append(pred)
         for data in self.eval_dataset:
-            ds = tf.data.Dataset.from_tensor_slices(data)
-            ds = ds.batch(BATCH_SIZE)
-            for mini_batch in ds:
-                pred = self.model([mini_batch], training=False)
-                pred_s.append(pred)
+            print(data[0].shape, data[1].shape)
+            pred = self.model([data])
+            print(pred.shape, pred.numpy().min(), pred.numpy().max())
 
         preds = np.concatenate(pred_s)
         preds = preds[:,0]
@@ -1111,7 +1107,7 @@ def run_evaluate_static_avg(data_dct, ll, cc, ckpt_dir_s_path, day_night='DAY',
     return ice_lons, ice_lats, preds_2d
 
 
-def run_evaluate_static(data_dct, num_tiles, ckpt_dir_s_path, day_night='DAY', l1b_or_l2='both', prob_thresh=0.5,
+def run_evaluate_static_fcn(data_dct, ckpt_dir_s_path, day_night='DAY', l1b_or_l2='both', prob_thresh=0.5,
                             flight_levels=[0, 1, 2, 3, 4], use_flight_altitude=False):
 
     ckpt_dir_s = os.listdir(ckpt_dir_s_path)
@@ -1124,7 +1120,7 @@ def run_evaluate_static(data_dct, num_tiles, ckpt_dir_s_path, day_night='DAY', l
     preds_dct = {flvl: None for flvl in flight_levels}
 
     nn = IcingIntensityFCN(day_night=day_night, l1b_or_l2=l1b_or_l2, use_flight_altitude=use_flight_altitude)
-    nn.num_data_samples = num_tiles
+    nn.num_data_samples = 1
     nn.build_model()
     nn.build_training()
     nn.build_evaluation()
@@ -1135,7 +1131,7 @@ def run_evaluate_static(data_dct, num_tiles, ckpt_dir_s_path, day_night='DAY', l
 
     for flvl in flight_levels:
         nn.flight_level = flvl
-        nn.setup_eval_pipeline(data_dct, num_tiles)
+        nn.setup_eval_pipeline(data_dct)
         nn.do_evaluate(ckpt_dir)
 
         probs = nn.test_probs