bug fix

model_zoo/research/cv/SRGAN/README.md

@@ -41,7 +41,7 @@ Validation and eval evaluationdataset used: [Set5](<http://people.rennes.inria.f
 
 The process of training SRGAN needs a pretrained VGG19 based on Imagenet.
 
-[Training scripts](<https://gitee.com/mindspore/mindspore/tree/r1.2/model_zoo/official/cv/vgg16>)|
+[Training scripts](<https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/vgg16>)|
 [VGG19 pretrained model](<https://download.mindspore.cn/model_zoo/>)
 
 # [Environment Requirements](#contents)
@@ -51,8 +51,8 @@ The process of training SRGAN needs a pretrained VGG19 based on Imagenet.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
-    - [MindSpore Tutorials](https://www.mindspore.cn/tutorials/en/master/index.html)
-    - [MindSpore Python API](https://www.mindspore.cn/docs/api/en/master/index.html)
+    - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
+    - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
 
 # [Script Description](#contents)
 
@@ -97,13 +97,16 @@ SRGAN
 # distributed training
 Usage: sh run_distribute_train.sh [DEVICE_NUM] [DISTRIBUTE] [RANK_TABLE_FILE] [LRPATH] [GTPATH] [VGGCKPT] [VLRPATH] [VGTPATH]
 
+eg: sh run_distribute_train.sh 8 1 ./hccl_8p.json ./DIV2K_train_LR_bicubic/X4 ./DIV2K_train_HR ./vgg.ckpt ./Set5/LR ./Set5/HR
 # standalone training
 Usage: sh run_standalone_train.sh [DEVICE_ID] [LRPATH] [GTPATH] [VGGCKPT] [VLRPATH] [VGTPATH]
+
+eg: sh run_distribute_train.sh 0 ./DIV2K_train_LR_bicubic/X4 ./DIV2K_train_HR ./vgg.ckpt ./Set5/LR ./Set5/HR
 ```
 
 ### [Training Result](#content)
 
-Training result will be stored in scripts/srgan0/ckpt. You can find checkpoint file.
+Training result will be stored in scripts/train_parallel0/ckpt. You can find checkpoint file.
 
 ### [Evaluation Script Parameters](#content)
 
@@ -111,7 +114,9 @@ Training result will be stored in scripts/srgan0/ckpt. You can find checkpoint f
 
 ```bash
 # evaling
-sh run_eval.sh [CKPT] [EVALLRPATH] [EVALGTPATH]
+sh run_eval.sh [CKPT] [EVALLRPATH] [EVALGTPATH] [DEVICE_ID]
+
+eg: sh run_eval.sh ./ckpt/best.ckpt ./Set14/LR ./Set14/HR 0
 ```
 
 ### [Evaluation result](#content)
@@ -153,4 +158,4 @@ Evaluation result will be stored in the scripts/result. Under this, you can find
 
 # [ModelZoo Homepage](#contents)
 
-Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
+Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

model_zoo/research/cv/SRGAN/eval.py

@@ -14,12 +14,10 @@
 # ============================================================================
 
 """file for evaling"""
-import os
 import argparse
 import numpy as np
 from skimage.color import rgb2ycbcr
 from skimage.metrics import peak_signal_noise_ratio
-from PIL import Image
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.common import set_seed
 from mindspore import context
@@ -34,7 +32,7 @@ parser.add_argument("--test_LR_path", type=str, default='/data/Set14/LR')
 parser.add_argument("--test_GT_path", type=str, default='/data/Set14/HR')
 parser.add_argument("--res_num", type=int, default=16)
 parser.add_argument("--scale", type=int, default=4)
-parser.add_argument("--generator_path", type=str, default='./scripts/srgan0/ckpt/G_model_1000.ckpt')
+parser.add_argument("--generator_path", type=str, default='./ckpt/best.ckpt')
 parser.add_argument("--mode", type=str, default='train')
 parser.add_argument("--device_id", type=int, default=0, help="device id, default: 0.")
 i = 0
@@ -47,9 +45,6 @@ if __name__ == '__main__':
     params = load_checkpoint(args.generator_path)
     load_param_into_net(generator, params)
     op = ops.ReduceSum(keep_dims=False)
-    if not os.path.exists("./result/Set14"):
-        os.makedirs("./result/Set14")
-    weizhi = './result/Set14/psnr.txt'
     psnr_list = []
 
     print("=======starting test=====")
@@ -78,16 +73,4 @@ if __name__ == '__main__':
 
         psnr = peak_signal_noise_ratio(y_output / 255.0, y_gt / 255.0, data_range=1.0)
         psnr_list.append(psnr)
-        psnr = str(psnr)
-        with open(weizhi, "w") as f:
-            f.write('psnr : %s \n' % psnr)
-
-        result = Image.fromarray((output * 255.0).astype(np.uint8))
-        result.save('./result/Set14/res_%04d.png'%i)
-        i = i+1
-        mean = np.mean(psnr_list)
-        mean = str(mean)
-    print("avg PSNR:")
-    print(mean)
-    with open(weizhi, "w") as f:
-        f.write('avg psnr : %s \n' % mean)
+    print("avg PSNR:", np.mean(psnr_list))

model_zoo/research/cv/SRGAN/scripts/run_eval.sh

@@ -12,18 +12,23 @@
 # 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.
-if [ $# != 3 ]
+if [ $# != 4 ]
 then
-    echo "Usage: sh run_eval.sh [CKPT] [EVALLRPATH] [EVALGTPATH]"
+    echo "Usage: sh run_eval.sh [CKPT] [EVALLRPATH] [EVALGTPATH] [DEVICE_ID]"
     exit 1
 fi
 
 export CKPT=$1
 export EVALLRPATH=$2
 export EVALGTPATH=$3
+export DEVICE_ID=$4
+
+rm -rf ./eval
+mkdir ./eval
+cp -r ../src ./eval
+cp -r ../*.py ./eval
+cd ./eval || exit
 
 env > env.log
-if [ $# == 3 ]
-then
-python ../eval.py --generator_path=$CKPT --test_LR_path=$EVALLRPATH --test_GT_path=$EVALGTPATH &> log &
-fi
+python ./eval.py --generator_path=$CKPT --test_LR_path=$EVALLRPATH --device_id $DEVICE_ID\
+                 --test_GT_path=$EVALGTPATH &> log &

model_zoo/research/cv/SRGAN/scripts/run_standalone_train.sh

@@ -35,7 +35,7 @@ cp -r ../src ./train_standalone
 cp -r ../*.py ./train_standalone
 cd ./train_standalone || exit
 
-echo "start traning"
+echo "start training"
 env > env.log
 if [ $# == 6 ]
 then

model_zoo/research/cv/SRGAN/train.py

@@ -113,7 +113,7 @@ if __name__ == '__main__':
         os.makedirs("./ckpt")
     print('start training:')
 
-    print('tart training PSNR:')
+    print('start training PSNR:')
     # warm up generator
     for epoch in range(args.start_psnr_epoch, args.psnr_epochs):
         print("training {:d} epoch:".format(epoch+1))
@@ -124,7 +124,6 @@ if __name__ == '__main__':
             mse_loss = train_psnr(hr, lr)
         steps = train_ds.get_dataset_size()
         time_elapsed = (time.time()-mysince)
-        print('the epoch needs time:{:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
         step_time = time_elapsed / steps
         print('per step needs time:{:.0f}ms'.format(step_time * 1000))
         print("mse_loss:")
@@ -192,7 +191,7 @@ if __name__ == '__main__':
     discriminator_optimizer = nn.Adam(discriminator.trainable_params(), 1e-4)
     train_discriminator = TrainOneStepD(discriminator_loss, discriminator_optimizer)
     train_generator = TrainOnestepG(generator_loss, generator_optimizer)
-
+    print("========================================")
     print('start training GAN :')
     # trainGAN
     for epoch in range(args.start_gan_epoch, args.gan_epochs):
@@ -205,7 +204,6 @@ if __name__ == '__main__':
             G_loss = train_generator(hr, lr)
         time_elapsed1 = (time.time()-mysince1)
         steps = train_ds.get_dataset_size()
-        print('the epoch needs time:{:.0f}m {:.0f}s'.format(time_elapsed1 // 60, time_elapsed1 % 60))
         step_time1 = time_elapsed1 / steps
         print('per step needs time:{:.0f}ms'.format(step_time1 * 1000))
         print("D_loss:")