update main_predict.py

video_prediction_tools/main_scripts/main_predict.py

@@ -80,7 +80,7 @@ class Postprocess(TrainModel):
         self.run_mode = run_mode
         self.data_mode = data_mode
         #self.channel = channel
-        #self.lquick = lquick
+        self.lquick = lquick
         #self.frac_data = frac_data
         # Attributes set during runtime
         #self.norm_cls = None
@@ -116,6 +116,9 @@ class Postprocess(TrainModel):
         #self.cond_quantile_vars = self.init_cond_quantile_vars()
         # setup test dataset and model
         self.test_dataset, self.num_samples_per_epoch = self.setup_dataset()
+        self.lats = self.test_dataset.lats
+        self.lons = self.test_dataset.lons
+        self.vars_in = self.test_dataset.variables
         # if lquick and self.test_dataset.shuffled:
         #     self.num_samples_per_epoch = Postprocess.reduce_samples(self.num_samples_per_epoch, frac_data)
         # self.num_samples_per_epoch = 100              # reduced number of epoch samples -> useful for testing
@@ -569,12 +572,11 @@ class Postprocess(TrainModel):
                               
             input_results = self.sess.run(self.input_iter)    
             t_starts = self.sess.run(self.ts_iter)
-            print('self.input_iter: {}'.format(self.input_iter))
-            print('input_results: {}'.format(input_results))
+            # print('self.input_iter: {}'.format(self.input_iter.items()))
             # t_starts = input_results["T_start"]   
                                                                                          
             # feed_dict = {input_ph: input_results[name] for name, input_ph in self.input_iter.items()}
-            feed_dict = {"x": input_results}
+            feed_dict = {"IteratorGetNext:0": input_results}
             gen_images = self.sess.run(self.video_model.outputs['gen_images'], feed_dict=feed_dict)
 
             # sanity check on length of forecast sequence
@@ -679,20 +681,20 @@ class Postprocess(TrainModel):
         """
         method = Postprocess.get_init_time.__name__
 
-        t_starts = np.squeeze(np.asarray(t_starts))
-        if not np.ndim(t_starts) == 1:
-            raise ValueError("%{0}: Inputted t_starts must be a 1D list/array of date-strings with format %Y%m%d%H"
-                             .format(method))
+        #t_starts = np.squeeze(np.asarray(t_starts))
+        #print('t_starts: {}'.format(t_starts))
+        #if not np.ndim(t_starts) == 1:
+        #    raise ValueError("%{0}: Inputted t_starts must be a 1D list/array of date-strings with format %Y%m%d%H"
+        #                     .format(method))
         for i, t_start in enumerate(t_starts):
             try:
-                #seq_ts = pd.date_range(dt.datetime.strptime(str(t_start), "%Y%m%d%H%M"), periods=self.context_frames,
-                #                      freq="10min")
-                print('t_start: ',t_start)
-                t0 = pd.date_range(dt.datetime.strptime(str(t_start), "%Y%m%d%H%M"), periods=4,
-                                       freq="-10min")
-                t1 = pd.date_range(dt.datetime.strptime(str(t_start), "%Y%m%d%H%M"),periods=self.context_frames-3,
+                seq_ts = pd.date_range(dt.datetime.strptime(str(t_start[0])[2:-1], "%Y-%m-%dT%H:%M:00"), periods=self.context_frames,
                                       freq="10min")
-                seq_ts = t0.append(t1)[1:]
+                #t0 = pd.date_range(dt.datetime.strptime(str(t_start), "%Y-%m-%dT%H:%M:00"), periods=4,
+                #                       freq="-10min")
+                #t1 = pd.date_range(dt.datetime.strptime(str(t_start), "%Y-%m-%dT%H:%M:00"),periods=self.context_frames-3,
+                #                       freq="10min")
+                #seq_ts = t0.append(t1)[1:]
                 print('seq_ts: ',seq_ts)
             except Exception as err:
                 print("%{0}: Could not convert {1} to datetime object. Ensure that the date-string format is 'Y%m%d%H'".

video_prediction_tools/model_modules/video_prediction/datasets/gzaws_dataset.py

@@ -86,6 +86,9 @@ class GZawsDataset(BaseDataset):
         self.n_samples = data_arr.shape[0]
         self.n_vars = len(self.variables)
 
+        self.lons = ds["lon"].values
+        self.lats = ds["lat"].values
+    
         return data_arr, init_times
 
     def make_dataset(self):