refactor avg_model; fix set_value not support start==end

deepspeech/utils/tensor_utils.py

@@ -94,9 +94,19 @@ def pad_sequence(sequences: List[paddle.Tensor],
         length = tensor.shape[0]
         # use index notation to prevent duplicate references to the tensor
         if batch_first:
-            out_tensor[i, :length, ...] = tensor
+            # TODO (Hui Zhang): set_value op not supprot `end==start`
+            # out_tensor[i, :length, ...] = tensor
+            if length != 0:
+                out_tensor[i, :length, ...] = tensor
+            else:
+                out_tensor[i, length, ...] = tensor
         else:
-            out_tensor[:length, i, ...] = tensor
+            # TODO (Hui Zhang): set_value op not supprot `end==start`
+            # out_tensor[:length, i, ...] = tensor
+            if length != 0:
+                out_tensor[:length, i, ...] = tensor
+            else:
+                out_tensor[length, i, ...] = tensor
 
     return out_tensor
 

utils/avg_model.py

@@ -27,33 +27,33 @@ def main(args):
     val_scores = []
     beat_val_scores = []
     selected_epochs = []
-    if args.val_best:
-        jsons = glob.glob(f'{args.ckpt_dir}/[!train]*.json')
-        for y in jsons:
-            with open(y, 'r') as f:
-                dic_json = json.load(f)
-            loss = dic_json['val_loss']
-            epoch = dic_json['epoch']
-            if epoch >= args.min_epoch and epoch <= args.max_epoch:
-                val_scores.append((epoch, loss))
 
-        val_scores = np.array(val_scores)
+    jsons = glob.glob(f'{args.ckpt_dir}/[!train]*.json')
+    jsons = sorted(jsons, key=os.path.getmtime, reverse=True)
+    for y in jsons:
+        with open(y, 'r') as f:
+            dic_json = json.load(f)
+        loss = dic_json['val_loss']
+        epoch = dic_json['epoch']
+        if epoch >= args.min_epoch and epoch <= args.max_epoch:
+            val_scores.append((epoch, loss))
+    val_scores = np.array(val_scores)
+
+    if args.val_best:
         sort_idx = np.argsort(val_scores[:, 1])
         sorted_val_scores = val_scores[sort_idx]
-        path_list = [
-            args.ckpt_dir + '/{}.pdparams'.format(int(epoch))
-            for epoch in sorted_val_scores[:args.num, 0]
-        ]
-
-        beat_val_scores = sorted_val_scores[:args.num, 1]
-        selected_epochs = sorted_val_scores[:args.num, 0].astype(np.int64)
-        print("best val scores = " + str(beat_val_scores))
-        print("selected epochs = " + str(selected_epochs))
     else:
-        path_list = glob.glob(f'{args.ckpt_dir}/[!avg][!final]*.pdparams')
-        path_list = sorted(path_list, key=os.path.getmtime)
-        path_list = path_list[-args.num:]
+        sorted_val_scores = val_scores
+
+    beat_val_scores = sorted_val_scores[:args.num, 1]
+    selected_epochs = sorted_val_scores[:args.num, 0].astype(np.int64)
+    print("selected val scores = " + str(beat_val_scores))
+    print("selected epochs = " + str(selected_epochs))
 
+    path_list = [
+        args.ckpt_dir + '/{}.pdparams'.format(int(epoch))
+        for epoch in sorted_val_scores[:args.num, 0]
+    ]
     print(path_list)
 
     avg = None
@@ -78,10 +78,11 @@ def main(args):
     meta_path = os.path.splitext(args.dst_model)[0] + '.avg.json'
     with open(meta_path, 'w') as f:
         data = json.dumps({
+            "mode": 'val_best' if args.val_best else 'latest',
             "avg_ckpt": args.dst_model,
             "ckpt": path_list,
-            "epoch": selected_epochs,
-            "val_loss": beat_val_scores,
+            "epoch": selected_epochs.tolist(),
+            "val_loss": beat_val_scores.tolist(),
         })
         f.write(data + "\n")