modify pinns network scripts

model_zoo/official/cv/psenet/README.md

@@ -104,7 +104,7 @@ After installing MindSpore via the official website, you can start training and
 bash scripts/run_distribute_train.sh [RANK_TABLE_FILE] [PRED_TRAINED PATH] [TRAIN_ROOT_DIR]
 
 #enter the path ,run Makefile
-cd ./src/ETSNET/pse/;make
+cd ./src/PSENET/pse/;make
 
 #run test.py
 python test.py --ckpt pretrained_model.ckpt --TEST_ROOT_DIR [test root path]
@@ -453,7 +453,7 @@ time_cb = TimeMonitor(data_size=step_size)
 loss_cb = LossCallBack(per_print_times=20)
 # set and apply parameters of check point
 ckpoint_cf = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=2)
-ckpoint_cb = ModelCheckpoint(prefix="ETSNet", config=ckpoint_cf, directory=config.TRAIN_MODEL_SAVE_PATH)
+ckpoint_cb = ModelCheckpoint(prefix="PSENet", config=ckpoint_cf, directory=config.TRAIN_MODEL_SAVE_PATH)
 
 model = Model(net)
 model.train(config.TRAIN_REPEAT_NUM, ds, dataset_sink_mode=False, callbacks=[time_cb, loss_cb, ckpoint_cb])

model_zoo/official/cv/psenet/README_CN.md

@@ -105,7 +105,7 @@ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib64
 bash scripts/run_distribute_train.sh [RANK_FILE] [PRETRAINED_PATH] [TRAIN_ROOT_DIR]
 
 # 进入路径，运行Makefile
-cd ./src/ETSNET/pse/;make clean&&make
+cd ./src/PSENET/pse/;make clean&&make
 
 # 运行test.py
 python test.py --ckpt [CKPK_PATH] --TEST_ROOT_DIR [TEST_DATA_DIR]
@@ -391,7 +391,7 @@ loss_cb = LossCallBack(per_print_times=20)
 
 # 设置并应用检查点参数
 ckpoint_cf = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=2)
-ckpoint_cb = ModelCheckpoint(prefix="ETSNet", config=ckpoint_cf, directory=config.TRAIN_MODEL_SAVE_PATH)
+ckpoint_cb = ModelCheckpoint(prefix="PSENet", config=ckpoint_cf, directory=config.TRAIN_MODEL_SAVE_PATH)
 
 model = Model(net)
 model.train(config.TRAIN_REPEAT_NUM, ds, dataset_sink_mode=False, callbacks=[time_cb, loss_cb, ckpoint_cb])

model_zoo/official/cv/psenet/mindspore_hub_conf.py

@@ -13,12 +13,12 @@
 # limitations under the License.
 # ============================================================================
 """hub config."""
-from src.ETSNET.etsnet import ETSNet
+from src.PSENET.psenet import PSENet
 from src.config import config
 
 def create_network(name, *args, **kwargs):
     if name == "psenet":
         infer_mode = kwargs.get("infer_mode", False)
         config.INFERENCE = infer_mode
-        return ETSNet(config)
+        return PSENet(config)
     raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/cv/psenet/test.py

@@ -83,7 +83,7 @@ def modelarts_pre_process():
     cmake_command = 'cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL=/usr/local ..&&make -j16&&sudo make install'
     os.system('cd {}/opencv-3.4.9&&mkdir build&&cd ./build&&{}'.format(local_path, cmake_command))
 
-    os.system('cd {}/src/ETSNET/pse&&make clean&&make'.format(local_path))
+    os.system('cd {}/src/PSENET/pse&&make clean&&make'.format(local_path))
     os.system('cd {}&&sed -i ’s/\r//‘ scripts/run_eval_ascend.sh'.format(local_path))
 
 
@@ -94,7 +94,7 @@ def modelarts_post_process():
 
 @moxing_wrapper(pre_process=modelarts_pre_process, post_process=modelarts_post_process)
 def test():
-    from src.ETSNET.pse import pse
+    from src.PSENET.pse import pse
 
     local_path = ""
     if config.enable_modelarts:

model_zoo/research/hpc/pinns/src/NavierStokes/train_ns.py

@@ -13,16 +13,59 @@
 # limitations under the License.
 # ============================================================================
 """Train PINNs for Navier-Stokes equation scenario"""
+import glob
+import os
+import shutil
 import numpy as np
 from mindspore import Model, context, nn
 from mindspore.common import set_seed
 from mindspore.train.callback import (CheckpointConfig, LossMonitor,
-                                      ModelCheckpoint, TimeMonitor)
+                                      ModelCheckpoint, TimeMonitor, Callback)
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from src.NavierStokes.dataset import generate_training_set_navier_stokes
 from src.NavierStokes.loss import PINNs_loss_navier
 from src.NavierStokes.net import PINNs_navier
 
 
+class EvalCallback(Callback):
+    """eval callback."""
+    def __init__(self, data_path, ckpt_dir, per_eval_epoch, num_neuron=20):
+        super(EvalCallback, self).__init__()
+        if not isinstance(per_eval_epoch, int) or per_eval_epoch <= 0:
+            raise ValueError("per_eval_epoch must be int and > 0")
+        layers = [3, num_neuron, num_neuron, num_neuron, num_neuron, num_neuron, num_neuron, num_neuron,
+                  num_neuron, 2]
+        _, lb, ub = generate_training_set_navier_stokes(10, 10, data_path, 0)
+        self.network = PINNs_navier(layers, lb, ub)
+        self.ckpt_dir = ckpt_dir
+        self.per_eval_epoch = per_eval_epoch
+        self.best_result = None
+
+    def epoch_end(self, run_context):
+        """epoch end function."""
+        cb_params = run_context.original_args()
+        cur_epoch = cb_params.cur_epoch_num
+        batch_num = cb_params.batch_num
+        if cur_epoch % self.per_eval_epoch == 0:
+            ckpt_format = os.path.join(self.ckpt_dir,
+                                       "checkpoint_PINNs_NavierStokes*-{}_{}.ckpt".format(cur_epoch, batch_num))
+            ckpt_list = glob.glob(ckpt_format)
+            if not ckpt_list:
+                raise ValueError("can not find {}".format(ckpt_format))
+            ckpt_name = sorted(ckpt_list)[-1]
+            print("the latest ckpt_name is", ckpt_name)
+            param_dict = load_checkpoint(ckpt_name)
+            load_param_into_net(self.network, param_dict)
+            lambda1_pred = self.network.lambda1.asnumpy()
+            lambda2_pred = self.network.lambda2.asnumpy()
+            error1 = np.abs(lambda1_pred - 1.0) * 100
+            error2 = np.abs(lambda2_pred - 0.01) / 0.01 * 100
+            print(f'Error of lambda 1 is {error1[0]:.6f}%')
+            print(f'Error of lambda 2 is {error2[0]:.6f}%')
+            if self.best_result is None or error1 + error2 < self.best_result:
+                self.best_result = error1 + error2
+                shutil.copyfile(ckpt_name, os.path.join(self.ckpt_dir, "best_result.ckpt"))
+
 def train_navier(epoch, lr, batch_size, n_train, path, noise, num_neuron, ck_path, seed=None):
     """
     Train PINNs for Navier-Stokes equation
@@ -57,9 +100,10 @@ def train_navier(epoch, lr, batch_size, n_train, path, noise, num_neuron, ck_pat
     # save model
     config_ck = CheckpointConfig(save_checkpoint_steps=1000, keep_checkpoint_max=20)
     ckpoint = ModelCheckpoint(prefix="checkpoint_PINNs_NavierStokes", directory=ck_path, config=config_ck)
+    eval_cb = EvalCallback(data_path=path, ckpt_dir=ck_path, per_eval_epoch=100)
 
     model = Model(network=n, loss_fn=loss, optimizer=opt)
 
     model.train(epoch=epoch, train_dataset=training_set,
-                callbacks=[LossMonitor(loss_print_num), ckpoint, TimeMonitor(1)], dataset_sink_mode=True)
+                callbacks=[LossMonitor(loss_print_num), ckpoint, TimeMonitor(1), eval_cb], dataset_sink_mode=True)
     print('Training complete')