!12777 fix ci pynative case

From: @jojobugfree
Reviewed-by: @chujinjin,@jjfeing
Signed-off-by: @chujinjin

tests/st/pynative/test_pynative_resnet50_ascend.py

@@ -32,7 +32,36 @@ from mindspore.nn import Cell
 from mindspore.ops import operations as P
 from mindspore.ops import composite as CP
 from mindspore.nn.optim.momentum import Momentum
-from mindspore.nn.wrap.cell_wrapper import WithLossCell
+from mindspore.train.callback import LossMonitor, Callback
+from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
+from mindspore.train.loss_scale_manager import FixedLossScaleManager
+from mindspore.train.model import Model
+
+class MyTimeMonitor(Callback):
+    def __init__(self, data_size):
+        super(MyTimeMonitor, self).__init__()
+        self.data_size = data_size
+        self.total = 0
+
+    def epoch_begin(self, run_context):
+        self.epoch_time = time.time()
+
+    def epoch_end(self, run_context):
+        epoch_msseconds = (time.time()-self.epoch_time) * 1000
+        per_step_mssconds = epoch_msseconds / self.data_size
+        print("epoch time:{0}, per step time:{1}".format(epoch_msseconds, per_step_mssconds), flush=True)
+
+    def step_begin(self, run_context):
+        self.step_time = time.time()
+
+    def step_end(self, run_context):
+        step_msseconds = (time.time() - self.step_time) * 1000
+        if step_msseconds < 265:
+            self.total = self.total + 1
+        print(f"step time:{step_msseconds}", flush=True)
+
+    def good_step(self):
+        return self.total
 
 random.seed(1)
 np.random.seed(1)
@@ -303,12 +332,12 @@ def resnet50(batch_size, num_classes):
     return ResNet(ResidualBlock, num_classes, batch_size)
 
 
-def create_dataset(repeat_num=1, training=True, batch_size=32):
+def create_dataset(repeat_num=1, training=True, batch_size=32, num_samples=1600):
     data_home = "/home/workspace/mindspore_dataset"
     data_dir = data_home + "/cifar-10-batches-bin"
     if not training:
         data_dir = data_home + "/cifar-10-verify-bin"
-    data_set = ds.Cifar10Dataset(data_dir)
+    data_set = ds.Cifar10Dataset(data_dir, num_samples=num_samples)
 
     resize_height = 224
     resize_width = 224
@@ -385,33 +414,25 @@ def test_pynative_resnet50():
 
     batch_size = 32
     num_classes = 10
+    loss_scale = 128
+    total_step = 50
     net = resnet50(batch_size, num_classes)
-    criterion = CrossEntropyLoss()
     optimizer = Momentum(learning_rate=0.01, momentum=0.9,
                          params=filter(lambda x: x.requires_grad, net.get_parameters()))
+    data_set = create_dataset(repeat_num=1, training=True, batch_size=batch_size, num_samples=total_step * batch_size)
 
-    net_with_criterion = WithLossCell(net, criterion)
-    net_with_criterion.set_grad()
-    train_network = GradWrap(net_with_criterion)
-    train_network.set_train()
+    # define callbacks
+    time_cb = MyTimeMonitor(data_size=data_set.get_dataset_size())
+    loss_cb = LossMonitor()
+    cb = [time_cb, loss_cb]
 
-    step = 0
-    max_step = 21
-    exceed_num = 0
-    data_set = create_dataset(repeat_num=1, training=True, batch_size=batch_size)
-    for element in data_set.create_dict_iterator(num_epochs=1):
-        step = step + 1
-        if step > max_step:
-            break
-        start_time = time.time()
-        input_data = element["image"]
-        input_label = element["label"]
-        loss_output = net_with_criterion(input_data, input_label)
-        grads = train_network(input_data, input_label)
-        optimizer(grads)
-        end_time = time.time()
-        cost_time = end_time - start_time
-        print("======step: ", step, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time)
-        if step > 1 and cost_time > 0.25:
-            exceed_num = exceed_num + 1
-    assert exceed_num < 20
+    loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    loss_scale = FixedLossScaleManager(loss_scale=loss_scale, drop_overflow_update=False)
+    model = Model(net, loss_fn=loss, optimizer=optimizer, loss_scale_manager=loss_scale, metrics={'acc'},
+                  amp_level="O2", keep_batchnorm_fp32=False)
+
+    # train model
+    model.train(1, data_set, callbacks=cb,
+                sink_size=data_set.get_dataset_size(), dataset_sink_mode=True)
+
+    assert time_cb.good_step() > 10