fix bugs in bert example script

model_zoo/bert/pretrain_eval.py

@@ -19,7 +19,7 @@ Bert evaluation script.
 
 import os
 from src import BertModel, GetMaskedLMOutput
-from evaluation_config import cfg, bert_net_cfg
+from src.evaluation_config import cfg, bert_net_cfg
 import mindspore.common.dtype as mstype
 from mindspore import context
 from mindspore.common.tensor import Tensor
@@ -87,17 +87,18 @@ class BertPretrainEva(nn.Cell):
         self.cast = P.Cast()
 
 
-    def construct(self, input_ids, input_mask, token_type_id, masked_pos, masked_ids, nsp_label, masked_weights):
+    def construct(self, input_ids, input_mask, token_type_id, masked_pos, masked_ids, masked_weights, nsp_label):
         bs, _ = self.shape(input_ids)
         probs = self.bert(input_ids, input_mask, token_type_id, masked_pos)
         index = self.argmax(probs)
         index = self.reshape(index, (bs, -1))
         eval_acc = self.equal(index, masked_ids)
         eval_acc1 = self.cast(eval_acc, mstype.float32)
-        acc = self.mean(eval_acc1)
-        P.Print()(acc)
-        self.total += self.shape(probs)[0]
-        self.acc += self.sum(eval_acc1)
+        real_acc = eval_acc1 * masked_weights
+        acc = self.sum(real_acc)
+        total = self.sum(masked_weights)
+        self.total += total
+        self.acc += acc
         return acc, self.total, self.acc
 
 
@@ -107,8 +108,8 @@ def get_enwiki_512_dataset(batch_size=1, repeat_count=1, distribute_file=''):
     '''
     ds = de.TFRecordDataset([cfg.data_file], cfg.schema_file, columns_list=["input_ids", "input_mask", "segment_ids",
                                                                             "masked_lm_positions", "masked_lm_ids",
-                                                                            "next_sentence_labels",
-                                                                            "masked_lm_weights"])
+                                                                            "masked_lm_weights",
+                                                                            "next_sentence_labels"])
     type_cast_op = C.TypeCast(mstype.int32)
     ds = ds.map(input_columns="segment_ids", operations=type_cast_op)
     ds = ds.map(input_columns="input_mask", operations=type_cast_op)
@@ -143,7 +144,8 @@ def MLM_eval():
     Evaluate function
     '''
     _, dataset, net_for_pretraining = bert_predict()
-    net = Model(net_for_pretraining, eval_network=net_for_pretraining, eval_indexes=[0, 1, 2], metrics={myMetric()})
+    net = Model(net_for_pretraining, eval_network=net_for_pretraining, eval_indexes=[0, 1, 2],
+                metrics={'name': myMetric()})
     res = net.eval(dataset, dataset_sink_mode=False)
     print("==============================================================")
     for _, v in res.items():

model_zoo/bert/run_pretrain.py

@@ -66,6 +66,8 @@ def run_pretrain():
     parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")
     parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, "
                                                                                 "default is 1000.")
+    parser.add_argument("--train_steps", type=int, default=-1, help="Training Steps, default is -1, "
+                                                                    "meaning run all steps according to epoch number.")
     parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
     parser.add_argument("--data_dir", type=str, default="", help="Data path, it is better to use absolute path")
     parser.add_argument("--schema_dir", type=str, default="", help="Schema path, it is better to use absolute path")
@@ -93,11 +95,12 @@ def run_pretrain():
     ds, new_repeat_count = create_bert_dataset(args_opt.epoch_size, device_num, rank, args_opt.do_shuffle,
                                                args_opt.enable_data_sink, args_opt.data_sink_steps,
                                                args_opt.data_dir, args_opt.schema_dir)
-
+    if args_opt.train_steps > 0:
+        new_repeat_count = min(new_repeat_count, args_opt.train_steps // args_opt.data_sink_steps)
     netwithloss = BertNetworkWithLoss(bert_net_cfg, True)
 
     if cfg.optimizer == 'Lamb':
-        optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size() * ds.get_repeat_count(),
+        optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size() * new_repeat_count,
                          start_learning_rate=cfg.Lamb.start_learning_rate, end_learning_rate=cfg.Lamb.end_learning_rate,
                          power=cfg.Lamb.power, warmup_steps=cfg.Lamb.warmup_steps, weight_decay=cfg.Lamb.weight_decay,
                          eps=cfg.Lamb.eps)
@@ -106,7 +109,7 @@ def run_pretrain():
                              momentum=cfg.Momentum.momentum)
     elif cfg.optimizer == 'AdamWeightDecayDynamicLR':
         optimizer = AdamWeightDecayDynamicLR(netwithloss.trainable_params(),
-                                             decay_steps=ds.get_dataset_size() * ds.get_repeat_count(),
+                                             decay_steps=ds.get_dataset_size() * new_repeat_count,
                                              learning_rate=cfg.AdamWeightDecayDynamicLR.learning_rate,
                                              end_learning_rate=cfg.AdamWeightDecayDynamicLR.end_learning_rate,
                                              power=cfg.AdamWeightDecayDynamicLR.power,

model_zoo/bert/src/cluener_evaluation.py

@@ -19,8 +19,8 @@ import json
 import numpy as np
 import mindspore.common.dtype as mstype
 from mindspore.common.tensor import Tensor
-import tokenization
-from sample_process import label_generation, process_one_example_p
+from . import tokenization
+from .sample_process import label_generation, process_one_example_p
 from .evaluation_config import cfg
 from .CRF import postprocess