add mannul random seed so we can reproduce the experimental results

examples/hi_xiaowen/s0/conf/mdtc_small.yaml

@@ -41,8 +41,7 @@ model:
 optim: adam
 optim_conf:
     lr: 0.001
-    weight_decay: 5e-5
+    weight_decay: 0.00005
-    warm_up_step: 2500
 
 training_config:
     grad_clip: 5

examples/hi_xiaowen/s0/path.sh

@@ -2,4 +2,4 @@ export PATH=$PWD:$PATH
 
 # NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
 export PYTHONIOENCODING=UTF-8
-export PYTHONPATH=../../:$PYTHONPATH
+export PYTHONPATH=../../../:$PYTHONPATH

kws/bin/train.py

@@ -30,6 +30,7 @@ from kws.dataset.dataset import Dataset
 from kws.utils.checkpoint import load_checkpoint, save_checkpoint
 from kws.model.kws_model import init_model
 from kws.utils.executor import Executor
+from kws.utils.train_utils import count_parameters, set_mannul_seed
 
 
 def get_args():
@@ -42,6 +43,7 @@ def get_args():
                         default=-1,
                         help='gpu id for this local rank, -1 for cpu')
     parser.add_argument('--model_dir', required=True, help='save model dir')
+    parser.add_argument('--seed', dest='seed', default=777, help='random seed')
     parser.add_argument('--checkpoint', help='checkpoint model')
     parser.add_argument('--tensorboard_dir',
                         default='tensorboard',
@@ -101,6 +103,7 @@ def main():
     logging.basicConfig(level=logging.DEBUG,
                         format='%(asctime)s %(levelname)s %(message)s')
     os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
+    set_mannul_seed(args.gpu)
     # Set random seed
     torch.manual_seed(777)
     print(args)
@@ -155,7 +158,7 @@ def main():
     # Init asr model from configs
     model = init_model(configs['model'])
     print(model)
-    num_params = sum(p.numel() for p in model.parameters())
+    num_params = count_parameters(model)
     print('the number of model params: {}'.format(num_params))
 
     # !!!IMPORTANT!!!
@@ -192,7 +195,9 @@ def main():
         device = torch.device('cuda' if use_cuda else 'cpu')
         model = model.to(device)
 
-    optimizer = optim.Adam(model.parameters(), **configs['optim_conf'])
+    optimizer = optim.Adam(model.parameters(),
+                           lr=configs['optim_conf']['lr'],
+                           weight_decay=configs['optim_conf']['weight_decay'])
     scheduler = optim.lr_scheduler.ReduceLROnPlateau(
         optimizer,
         mode='min',

kws/model/kws_model.py

@@ -56,7 +56,6 @@ class KWSModel(torch.nn.Module):
             x = self.preprocessing(x)
         x, _ = self.backbone(x)
         x = self.classifier(x)
-        x = torch.sigmoid(x)
         return x
 
 

kws/model/loss.py

@@ -37,6 +37,7 @@ def max_polling_loss(logits: torch.Tensor,
         (float): loss of current batch
         (float): accuracy of current batch
     '''
+    logits = torch.sigmoid(logits)
     mask = padding_mask(lengths)
     num_utts = logits.size(0)
     num_keywords = logits.size(2)

kws/utils/train_utils.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python3
+# Copyright (c) 2021 Jingyong Hou (houjingyong@gmail.com)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import numpy as np
+import random
+
+
+def set_mannul_seed(seed):
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+
+
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)