fix STGAN log save

research/cv/STGAN/README.md

@@ -69,22 +69,22 @@ After installing MindSpore via the official website, you can start training and
 
     ```python
     # train STGAN
-    sh scripts/run_standalone_train.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID]
+    bash scripts/run_standalone_train.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID]
     # distributed training
-    sh scripts/run_distribute_train.sh [RANK_TABLE_FILE] [EXPERIMENT_NAME] [DATA_PATH]
+    bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [EXPERIMENT_NAME] [DATA_PATH]
     # evaluate STGAN
-    sh scripts/run_eval.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID] [CHECKPOINT_PATH]
+    bash scripts/run_eval.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID] [CHECKPOINT_PATH]
     ```
 
 - running on GPU
 
     ```python
     # train STGAN
-    sh scripts/run_standalone_train_gpu.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID]
+    bash scripts/run_standalone_train_gpu.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID]
     # distributed training
-    sh scripts/run_distribute_train_gpu.sh [EXPERIMENT_NAME] [DATA_PATH]
+    bash scripts/run_distribute_train_gpu.sh [EXPERIMENT_NAME] [DATA_PATH]
     # evaluate STGAN, if you want to evaluate distributed training result, you should enter ./train_parallel
-    sh scripts/run_eval_gpu.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID] [CHECKPOINT_PATH]
+    bash scripts/run_eval_gpu.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID] [CHECKPOINT_PATH]
     ```
 
 ## [Script Description](#contents)
@@ -143,9 +143,9 @@ Major parameters in train.py and utils/args.py as follows:
   ```bash
   python train.py --dataroot ./dataset --experiment_name 128 > log 2>&1 &
   # or run the script
-  sh scripts/run_standalone_train.sh ./dataset 128 0
+  bash scripts/run_standalone_train.sh ./dataset 128 0
   # distributed training
-  sh scripts/run_distribute_train.sh ./config/rank_table_8pcs.json 128 /data/dataset
+  bash scripts/run_distribute_train.sh ./config/rank_table_8pcs.json 128 /data/dataset
   ```
 
 - running on GPU
@@ -153,9 +153,9 @@ Major parameters in train.py and utils/args.py as follows:
   ```bash
   python train.py --dataroot ./dataset --experiment_name 128 --platform="GPU" > log 2>&1 &
   # or run the script
-  sh scripts/run_standalone_train_gpu.sh ./dataset 128 0
+  bash scripts/run_standalone_train_gpu.sh ./dataset 128 0
   # distributed training
-  sh scripts/run_distribute_train_gpu.sh 128 /data/dataset
+  bash scripts/run_distribute_train_gpu.sh 128 /data/dataset
   ```
 
   After training, the loss value will be achieved as follows:
@@ -183,7 +183,7 @@ Before running the command below, please check the checkpoint path used for eval
   ```bash
   python eval.py --dataroot ./dataset --experiment_name 128 > eval_log.txt 2>&1 &
   # or run the script
-  sh scripts/run_eval.sh ./dataset 128 0 ./ckpt/generator.ckpt
+  bash scripts/run_eval.sh ./dataset 128 0 ./ckpt/generator.ckpt
   ```
 
 - running on GPU
@@ -191,7 +191,7 @@ Before running the command below, please check the checkpoint path used for eval
   ```bash
   python eval.py --dataroot ./dataset --experiment_name 128 --platform="GPU" > eval_log.txt 2>&1 &
   # or run the script (if you want to evaluate distributed training result, you should enter ./train_parallel, then run the script)
-  sh scripts/run_eval_gpu.sh ./dataset 128 0 ./ckpt/generator.ckpt
+  bash scripts/run_eval_gpu.sh ./dataset 128 0 ./ckpt/generator.ckpt
   ```
 
   You can view the results in the output directory, which contains a batch of result sample images.

research/cv/STGAN/export.py

@@ -14,14 +14,13 @@
 # ============================================================================
 """ Model Export """
 import numpy as np
-from mindspore import context, Tensor
+from mindspore import Tensor
 from mindspore.train.serialization import export
 from src.models import STGANModel
 from src.utils import get_args
 
 if __name__ == '__main__':
     args = get_args("test")
-    context.set_context(mode=context.GRAPH_MODE, device_id=args.device_id)
     model = STGANModel(args)
     input_shp = [1, 3, 128, 128]
     input_shp_2 = [1, 4]

research/cv/STGAN/modelarts/models/stgan.py

@@ -151,17 +151,15 @@ class STGANModel(BaseModel):
                 self.train_G(self.real_x, self.label_org, self.label_trg, attr_diff)
             # saving losses
             if (self.current_iteration / 5) % self.args.print_freq == 0:
-                with open(os.path.join(self.train_log_path, 'loss.log'),
-                          'a+') as f:
-                    f.write('Iter: %s\n' % self.current_iteration)
-                    f.write(
-                        'loss D: %s, loss D_real: %s, loss D_fake: %s, loss D_gp: %s, loss D_adv: %s, loss D_cls: %s \n'
-                        % (loss_D, loss_real_D, loss_fake_D, loss_gp_D,
-                           loss_adv_D, loss_cls_D))
-                    f.write(
-                        'loss G: %s, loss G_rec: %s, loss G_fake: %s, loss G_adv: %s, loss G_cls: %s \n\n'
-                        % (loss_G, loss_rec_G, loss_fake_G, loss_adv_G,
-                           loss_cls_G))
+                print('Iter: %s\n' % self.current_iteration)
+                print(
+                    'loss D: %s, loss D_real: %s, loss D_fake: %s, loss D_gp: %s, loss D_adv: %s, loss D_cls: %s \n'
+                    % (loss_D, loss_real_D, loss_fake_D, loss_gp_D,
+                       loss_adv_D, loss_cls_D))
+                print(
+                    'loss G: %s, loss G_rec: %s, loss G_fake: %s, loss G_adv: %s, loss G_cls: %s \n\n'
+                    % (loss_G, loss_rec_G, loss_fake_G, loss_adv_G,
+                       loss_cls_G))
 
     def eval(self, data_loader):
         """ Eval function of STGAN

research/cv/STGAN/scripts/run_distribute_train.sh

@@ -17,7 +17,7 @@
 
 if [ $# != 3 ]
 then
-    echo "Usage: sh run_distribute_train.sh [RANK_TABLE_FILE] [EXPERIMENT_NAME] [DATA_PATH]"
+    echo "Usage: bash run_distribute_train.sh [RANK_TABLE_FILE] [EXPERIMENT_NAME] [DATA_PATH]"
 exit 1
 fi
 

research/cv/STGAN/scripts/run_distribute_train_gpu.sh

@@ -17,7 +17,7 @@
 
 if [ $# != 2 ]
 then
-    echo "Usage: sh run_distribute_train_gpu.sh [EXPERIMENT_NAME] [DATA_PATH]"
+    echo "Usage: bash run_distribute_train_gpu.sh [EXPERIMENT_NAME] [DATA_PATH]"
 exit 1
 fi
 

research/cv/STGAN/scripts/run_eval.sh

@@ -17,7 +17,7 @@
 
 if [ $# != 4 ]
 then
-    echo "Usage: sh run_eval.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID] [CHECKPOINT_PATH]"
+    echo "Usage: bash run_eval.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID] [CHECKPOINT_PATH]"
 exit 1
 fi
 

research/cv/STGAN/scripts/run_eval_gpu.sh

@@ -17,7 +17,7 @@
 
 if [ $# != 4 ]
 then
-    echo "Usage: sh run_eval_gpu.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID] [CHECKPOINT_PATH]"
+    echo "Usage: bash run_eval_gpu.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID] [CHECKPOINT_PATH]"
 exit 1
 fi
 

research/cv/STGAN/scripts/run_standalone_train.sh

@@ -17,7 +17,7 @@
 
 if [ $# != 3 ]
 then
-    echo "Usage: sh run_standalone_train.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID]"
+    echo "Usage: bash run_standalone_train.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID]"
 exit 1
 fi
 

research/cv/STGAN/scripts/run_standalone_train_gpu.sh

@@ -17,7 +17,7 @@
 
 if [ $# != 3 ]
 then
-    echo "Usage: sh run_standalone_train_gpu.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID]"
+    echo "Usage: bash run_standalone_train_gpu.sh [DATA_PATH] [EXPERIMENT_NAME] [DEVICE_ID]"
 exit 1
 fi
 

research/cv/STGAN/src/models/base_model.py

@@ -61,9 +61,12 @@ class BaseModel(ABC):
                       'w') as f:
                 f.write(json.dumps(vars(self.args)))
             if self.current_iteration == -1:
-                with open(os.path.join(self.config_save_path, 'latest.conf'),
-                          'r') as f:
-                    self.current_iteration = int(f.read())
+                if os.path.exists(os.path.join(self.config_save_path, 'latest.conf')):
+                    with open(os.path.join(self.config_save_path, 'latest.conf'),
+                              'r') as f:
+                        self.current_iteration = int(f.read())
+                else:
+                    self.current_iteration = 0
 
         # sample save path
         if self.isTrain:

research/cv/STGAN/src/models/networks.py

@@ -413,8 +413,7 @@ class TrainOneStepGenerator(nn.Cell):
         grads = self.grad(self.network, self.weights)(real_x, c_org, c_trg,
                                                       attr_diff, sens)
         grads = self.grad_reducer(grads)
-        self.optimizer(grads)
-        return (loss_G, fake_x, loss_G,
+        return (F.depend(loss_G, self.optimizer(grads)), fake_x, loss_G,
                 loss_fake_G, loss_cls_G, loss_rec_G, loss_adv_G)
 
 
@@ -452,6 +451,5 @@ class TrainOneStepDiscriminator(nn.Cell):
         grads = self.grad(self.network, self.weights)(real_x, c_org, c_trg,
                                                       attr_diff, alpha, sens)
         grads = self.grad_reducer(grads)
-        self.optimizer(grads)
-        return (loss_D, loss_D, loss_real_D,
+        return (F.depend(loss_D, self.optimizer(grads)), loss_D, loss_real_D,
                 loss_fake_D, loss_cls_D, loss_gp_D, loss_adv_D, attr_diff)

research/cv/STGAN/src/models/stgan.py

@@ -151,17 +151,15 @@ class STGANModel(BaseModel):
                 self.train_G(self.real_x, self.label_org, self.label_trg, attr_diff)
             # saving losses
             if (self.current_iteration / 5) % self.args.print_freq == 0:
-                with open(os.path.join(self.train_log_path, 'loss.log'),
-                          'a+') as f:
-                    f.write('Iter: %s\n' % self.current_iteration)
-                    f.write(
-                        'loss D: %s, loss D_real: %s, loss D_fake: %s, loss D_gp: %s, loss D_adv: %s, loss D_cls: %s \n'
-                        % (loss_D, loss_real_D, loss_fake_D, loss_gp_D,
-                           loss_adv_D, loss_cls_D))
-                    f.write(
-                        'loss G: %s, loss G_rec: %s, loss G_fake: %s, loss G_adv: %s, loss G_cls: %s \n\n'
-                        % (loss_G, loss_rec_G, loss_fake_G, loss_adv_G,
-                           loss_cls_G))
+                print('Iter: %s\n' % self.current_iteration)
+                print(
+                    'loss D: %s, loss D_real: %s, loss D_fake: %s, loss D_gp: %s, loss D_adv: %s, loss D_cls: %s \n'
+                    % (loss_D, loss_real_D, loss_fake_D, loss_gp_D,
+                       loss_adv_D, loss_cls_D))
+                print(
+                    'loss G: %s, loss G_rec: %s, loss G_fake: %s, loss G_adv: %s, loss G_cls: %s \n\n'
+                    % (loss_G, loss_rec_G, loss_fake_G, loss_adv_G,
+                       loss_cls_G))
 
     def eval(self, data_loader):
         """ Eval function of STGAN

research/cv/STGAN/src/utils/args.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 """arguments"""
-import os
 import argparse
 import ast
 import datetime
@@ -266,19 +265,7 @@ def get_args(phase):
         assert args.experiment_name != default_experiment_name, "--experiment_name should be assigned in test mode"
     if args.continue_train:
         assert args.experiment_name != default_experiment_name, "--experiment_name should be assigned in continue"
-    if args.device_num > 1 and args.platform == "Ascend":
-        context.set_context(mode=context.GRAPH_MODE,
-                            device_target=args.platform,
-                            save_graphs=args.save_graphs,
-                            device_id=int(os.environ["DEVICE_ID"]))
-        context.reset_auto_parallel_context()
-        context.set_auto_parallel_context(
-            parallel_mode=ParallelMode.DATA_PARALLEL,
-            gradients_mean=True,
-            device_num=args.device_num)
-        init()
-        args.rank = int(os.environ["DEVICE_ID"])
-    elif args.device_num > 1 and args.platform == "GPU":
+    if args.device_num > 1:
         init()
         context.reset_auto_parallel_context()
         args.rank = get_rank()

research/cv/STGAN/train.py

@@ -14,8 +14,6 @@
 # ============================================================================
 """ STGAN TRAIN"""
 import time
-import tqdm
-
 from mindspore.common import set_seed
 
 from src.models import STGANModel
@@ -45,9 +43,9 @@ def train():
     model = STGANModel(args)
     it_count = 0
 
-    for _ in tqdm.trange(args.n_epochs, desc='Epoch Loop', unit='epoch'):
+    for epoch in range(args.n_epochs):
         start_epoch_time = time.time()
-        for _ in tqdm.trange(iter_per_epoch, desc='Step Loop', unit='step'):
+        for _ in range(iter_per_epoch):
             if model.current_iteration > it_count:
                 it_count += 1
                 continue
@@ -66,14 +64,14 @@ def train():
                     model.eval(data_loader)
 
             except KeyboardInterrupt:
-                logger.info('You have entered CTRL+C.. Wait to finalize')
+                print('You have entered CTRL+C.. Wait to finalize')
                 model.save_networks()
 
             it_count += 1
             model.current_iteration = it_count
         if args.rank == 0:
-            with open('performance.log', "a") as f:
-                f.write('average speed: {}ms/step\n'.format((time.time() - start_epoch_time)*1000/iter_per_epoch))
+            print('epoch: {}, average speed: {}ms/step'.format(
+                epoch, (time.time() - start_epoch_time) * 1000 / iter_per_epoch))
     model.save_networks()
     print('\n\n=============== finish training ===============\n\n')