snapshot...

modules/deeplearning/espcn.py

@@ -360,9 +360,8 @@ class ESPCN:
         self.get_test_dataset([0])
         print('setup_test_pipeline: Done')
 
-    def setup_eval_pipeline(self, data_dct, num_tiles=1):
-        self.data_dct = data_dct
-        idxs = np.arange(num_tiles)
+    def setup_eval_pipeline(self, filename):
+        idxs = [0]
         self.num_data_samples = idxs.shape[0]
 
         self.get_evaluate_dataset(idxs)
@@ -398,26 +397,26 @@ class ESPCN:
         conv = tf.keras.layers.BatchNormalization()(conv)
         print(conv.shape)
 
-        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(conv)
-        conv = tf.keras.layers.BatchNormalization()(conv)
-        print(conv.shape)
-
-        conv = conv + skip
-        conv = tf.keras.layers.LeakyReLU()(conv)
-        print(conv.shape)
+        # conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(conv)
+        # conv = tf.keras.layers.BatchNormalization()(conv)
+        # print(conv.shape)
+        #
+        # conv = conv + skip
+        # conv = tf.keras.layers.LeakyReLU()(conv)
+        # print(conv.shape)
 
-        conv = tf.keras.layers.Conv2D(num_filters/2, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+        conv = tf.keras.layers.Conv2D(num_filters // 2, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
         print(conv.shape)
 
-        conv = tf.keras.layers.Conv2D(num_filters/2, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
+        conv = tf.keras.layers.Conv2D(num_filters // 2, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
         print(conv.shape)
 
         # conv = tf.keras.layers.Conv2D(4, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         # print(conv.shape)
 
-        conv = tf.keras.layers.Conv2DTranspose(4, kernel_size=3, strides=2, padding=padding, activation=activation)(conv)
+        conv = tf.keras.layers.Conv2DTranspose(num_filters // 4, kernel_size=3, strides=2, padding=padding, activation=activation)(conv)
         print(conv.shape)
 
         self.logits = tf.keras.layers.Conv2D(1, kernel_size=1, strides=1, padding=padding, name='probability', activation=tf.nn.sigmoid)(conv)
@@ -741,26 +740,21 @@ class ESPCN:
 
         self.test_preds = preds
 
-    def do_evaluate(self, prob_thresh=0.5):
+    def do_evaluate(self, nda_lr, ckpt_dir, prob_thresh=0.5):
 
-        self.reset_test_metrics()
+        ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=self.model)
+        ckpt_manager = tf.train.CheckpointManager(ckpt, ckpt_dir, max_to_keep=3)
 
-        pred_s = []
+        ckpt.restore(ckpt_manager.latest_checkpoint)
 
-        for data in self.eval_dataset:
-            print(data[0].shape, data[1].shape)
-            pred = self.model([data])
-            print(pred.shape, np.histogram(pred.numpy()))
+        data = normalize(nda_lr, data_param, mean_std_dct)
+        data = np.expand_dims(data, axis=0)
+        data = np.expand_dims(data, axis=3)
 
-        preds = np.concatenate(pred_s)
-        preds = preds[:,0]
-        self.test_probs = preds
+        self.reset_test_metrics()
 
-        if NumClasses == 2:
-            preds = np.where(preds > prob_thresh, 1, 0)
-        else:
-            preds = np.argmax(preds, axis=1)
-        self.test_preds = preds
+        pred = self.model([data])
+        self.test_probs = pred
 
     def run(self, directory):
         train_data_files = glob.glob(directory+'data_train*.npy')
@@ -783,8 +777,7 @@ class ESPCN:
         self.restore(ckpt_dir)
 
     def run_evaluate(self, filename, ckpt_dir):
-        data_dct, ll, cc = make_for_full_domain_predict(filename, name_list=self.train_params)
-        self.setup_eval_pipeline(data_dct, len(ll))
+        self.setup_eval_pipeline(filename)
         self.build_model()
         self.build_training()
         self.build_evaluation()