recitify wdsr scripts

model_zoo/research/cv/wdsr/README_CN.md

@@ -116,8 +116,8 @@ WDSR网络主要由几个基本模块（包括卷积层和池化层）组成。
 - 框架
     - [MindSpore](https://www.mindspore.cn/install/en)
 - 如需查看详情，请参见如下资源：
-    - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore教程](https://www.mindspore.cn/tutorial/training/zh-CN/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
 
 # 快速入门
 

model_zoo/research/cv/wdsr/script/run_ascend_standalone.sh

@@ -1,19 +1,3 @@
-#!/bin/bash
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-
 #!/bin/bash
 # Copyright 2020-2021 Huawei Technologies Co., Ltd
 #

model_zoo/research/cv/wdsr/src/args.py

@@ -52,8 +52,6 @@ parser.add_argument('--epochs', type=int, default=300,
                     help='number of epochs to train')
 parser.add_argument('--batch_size', type=int, default=16,
                     help='input batch size for training')
-#parser.add_argument('--self_ensemble', action='store_true',
-#                    help='use self-ensemble method for test')
 parser.add_argument('--test_only', action='store_true',
                     help='set this option to test the model')
 # Optimization specifications
@@ -61,6 +59,8 @@ parser.add_argument('--lr', type=float, default=1e-4,
                     help='learning rate')
 parser.add_argument('--init_loss_scale', type=float, default=65536.,
                     help='scaling factor')
+parser.add_argument('--loss_scale', type=float, default=1024.0,
+                    help='loss_scale')
 parser.add_argument('--decay', type=str, default='200',
                     help='learning rate decay type')
 parser.add_argument('--betas', type=tuple, default=(0.9, 0.999),

model_zoo/research/cv/wdsr/src/data/common.py

@@ -20,11 +20,8 @@ def get_patch(*args, patch_size=96, scale=2, input_large=False):
     ih, iw = args[0].shape[:2]
     tp = patch_size
     ip = tp // scale
-    # print("tp=%g,scale=%g,"%(tp,scale),end='',flush=True)
-    # print("ih=%g,iw=%g,ip=%g"%(ih,iw,ip),flush=True)
     ix = random.randrange(0, iw - ip + 1)
     iy = random.randrange(0, ih - ip + 1)
-    # ix = iy = 0
     if not input_large:
         tx, ty = scale * ix, scale * iy
     else:

model_zoo/research/cv/wdsr/src/data/srdata.py

@@ -106,13 +106,11 @@ class SRData:
             with open(f, 'wb') as _f:
                 pickle.dump(imageio.imread(img), _f)
 
-    # pylint: disable=unused-variable
     def __getitem__(self, idx):
-        lr, hr, filename = self._load_file(idx)
+        lr, hr, _ = self._load_file(idx)
         pair = self.get_patch(lr, hr)
         pair = common.set_channel(*pair, n_channels=self.args.n_colors)
         pair_t = common.np2Tensor(*pair, rgb_range=self.args.rgb_range)
-        #return pair_t[0], pair_t[1], [self.idx_scale], [filename]
         return pair_t[0], pair_t[1]
 
     def __len__(self):
@@ -163,7 +161,6 @@ class SRData:
     def _load_file(self, idx):
         """srdata"""
         idx = self._get_index(idx)
-        # print(idx,flush=True)
         f_hr = self.images_hr[idx]
         f_lr = self.images_lr[self.idx_scale][idx]
         filename, _ = os.path.splitext(os.path.basename(f_hr))

model_zoo/research/cv/wdsr/src/model.py

@@ -36,7 +36,6 @@ class Block(nn.Cell):
     """residual block"""
     def __init__(self):
         super(Block, self).__init__()
-        # wn = lambda x: mindspore.nn.GroupNorm(x)
         act = nn.ReLU()
         self.res_scale = 1
         body = []

model_zoo/research/cv/wdsr/src/utils.py

@@ -30,7 +30,7 @@ class Trainer():
         self.criterion = nn.L1Loss()
         self.loss_history = []
         self.begin_time = time.time()
-        self.optimizer = nn.Adam(self.model.trainable_params(), learning_rate=args.lr, loss_scale=1024.0)
+        self.optimizer = nn.Adam(self.model.trainable_params(), learning_rate=args.lr, loss_scale=args.loss_scale)
         self.loss_net = nn.WithLossCell(self.model, self.criterion)
         self.net = nn.TrainOneStepCell(self.loss_net, self.optimizer)
     def train(self, epoch):

model_zoo/research/cv/wdsr/train.py

@@ -56,7 +56,7 @@ def train_net():
     for i in range(0, args.epochs):
         cur_lr = args.lr / (2 ** ((i + 1)//200))
         lr.extend([cur_lr] * step_size)
-    opt = nn.Adam(net_m.trainable_params(), learning_rate=lr, loss_scale=1024.0)
+    opt = nn.Adam(net_m.trainable_params(), learning_rate=lr, loss_scale=args.loss_scale)
     loss = nn.L1Loss()
     loss_scale_manager = DynamicLossScaleManager(init_loss_scale=args.init_loss_scale, \
              scale_factor=2, scale_window=1000)