fix smoke of resnet thor

tests/st/networks/models/resnet50/src_thor/dataset.py

@@ -16,18 +16,15 @@
 """create train or eval dataset."""
 
 import os
-
 import mindspore.common.dtype as mstype
 import mindspore.dataset as ds
-import mindspore.dataset.transforms.c_transforms as C2
 import mindspore.dataset.vision.c_transforms as C
-
-ds.config.set_seed(1)
+import mindspore.dataset.transforms.c_transforms as C2
 
 
 def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
     """
-    Create a train or eval dataset.
+    create a train or eval dataset.
 
     Args:
         dataset_path(string): the path of dataset.
@@ -41,10 +38,14 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
 
     device_num = int(os.getenv("RANK_SIZE"))
     rank_id = int(os.getenv("RANK_ID"))
-    if device_num == 1:
-        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
+    if do_train:
+        if device_num == 1:
+            data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
+        else:
+            data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
+                                             num_shards=device_num, shard_id=rank_id)
     else:
-        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
+        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=False,
                                          num_shards=device_num, shard_id=rank_id)
 
     image_size = 224
@@ -54,8 +55,9 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
     # define map operations
     if do_train:
         trans = [
-            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
-            C.RandomHorizontalFlip(prob=0.5),
+            C.Decode(),
+            C.Resize((256, 256)),
+            C.CenterCrop(image_size),
             C.Normalize(mean=mean, std=std),
             C.HWC2CHW()
         ]

tests/st/networks/models/resnet50/test_resnet50_imagenet.py

@@ -39,6 +39,7 @@ from tests.st.networks.models.resnet50.src.config import config
 from tests.st.networks.models.resnet50.src.metric import DistAccuracy, ClassifyCorrectCell
 from tests.st.networks.models.resnet50.src.CrossEntropySmooth import CrossEntropySmooth
 from tests.st.networks.models.resnet50.src_thor.config import config as thor_config
+from tests.st.networks.models.resnet50.src_thor.dataset import create_dataset as create_dataset_thor
 from tests.st.networks.models.resnet50.src_thor.model_thor import Model as THOR_Model
 from tests.st.networks.models.resnet50.src_thor.resnet import resnet50 as resnet50_thor
 
@@ -250,8 +251,8 @@ def train_process_thor(q, device_id, epoch_size, device_num, enable_hccl):
                               num_classes=thor_config.class_num)
 
     # train dataset
-    dataset = create_dataset(dataset_path=dataset_path, do_train=True,
-                             repeat_num=1, batch_size=thor_config.batch_size)
+    dataset = create_dataset_thor(dataset_path=dataset_path, do_train=True,
+                                  repeat_num=1, batch_size=thor_config.batch_size)
 
     step_size = dataset.get_dataset_size()
     eval_interval = thor_config.eval_interval
@@ -367,5 +368,5 @@ def test_resnet_and_resnet_thor_imagenet_4p():
     for i in range(4, device_num + 4):
         os.system("rm -rf " + str(i))
     print("End training...")
-    assert thor_acc > 0.22
+    assert thor_acc > 0.25
     assert thor_cost < 25