Eval during train for ResNet (#52768)

* Eval during train for ResNet

test/prim/model/test_resnet_prim_cinn.py

@@ -131,31 +131,13 @@ def optimizer_setting(parameter_list=None):
     return optimizer
 
 
-def train(to_static, enable_prim, enable_cinn):
-    if core.is_compiled_with_cuda():
-        paddle.set_device('gpu')
-    else:
-        paddle.set_device('cpu')
-    np.random.seed(SEED)
-    paddle.seed(SEED)
-    paddle.framework.random._manual_program_seed(SEED)
-    fluid.core._set_prim_all_enabled(enable_prim)
-
-    train_reader = paddle.batch(
-        reader_decorator(paddle.dataset.flowers.train(use_xmap=False)),
-        batch_size=batch_size,
-        drop_last=True,
-    )
-    data_loader = fluid.io.DataLoader.from_generator(capacity=5, iterable=True)
-    data_loader.set_sample_list_generator(train_reader)
-
-    resnet = resnet50(False)
-    if to_static:
-        build_strategy = paddle.static.BuildStrategy()
-        if enable_cinn:
-            build_strategy.build_cinn_pass = True
-        resnet = paddle.jit.to_static(resnet, build_strategy=build_strategy)
-    optimizer = optimizer_setting(parameter_list=resnet.parameters())
+def run(model, data_loader, optimizer, mode):
+    if mode == 'train':
+        model.train()
+        end_step = 9
+    elif mode == 'eval':
+        model.eval()
+        end_step = 1
 
     for epoch in range(epoch_num):
         total_acc1 = 0.0
@@ -167,7 +149,7 @@ def train(to_static, enable_prim, enable_cinn):
             start_time = time.time()
             img, label = data
 
-            pred = resnet(img)
+            pred = model(img)
             avg_loss = paddle.nn.functional.cross_entropy(
                 input=pred,
                 label=label,
@@ -179,9 +161,10 @@ def train(to_static, enable_prim, enable_cinn):
             acc_top1 = paddle.static.accuracy(input=pred, label=label, k=1)
             acc_top5 = paddle.static.accuracy(input=pred, label=label, k=5)
 
-            avg_loss.backward()
-            optimizer.minimize(avg_loss)
-            resnet.clear_gradients()
+            if mode == 'train':
+                avg_loss.backward()
+                optimizer.minimize(avg_loss)
+                model.clear_gradients()
 
             total_acc1 += acc_top1
             total_acc5 += acc_top5
@@ -190,8 +173,9 @@ def train(to_static, enable_prim, enable_cinn):
 
             end_time = time.time()
             print(
-                "epoch %d | batch step %d, loss %0.8f, acc1 %0.3f, acc5 %0.3f, time %f"
+                "[%s]epoch %d | batch step %d, loss %0.8f, acc1 %0.3f, acc5 %0.3f, time %f"
                 % (
+                    mode,
                     epoch,
                     batch_id,
                     avg_loss,
@@ -200,7 +184,7 @@ def train(to_static, enable_prim, enable_cinn):
                     end_time - start_time,
                 )
             )
-            if batch_id >= 9:
+            if batch_id >= end_step:
                 # avoid dataloader throw abort signaal
                 data_loader._reset()
                 break
@@ -208,6 +192,38 @@ def train(to_static, enable_prim, enable_cinn):
     return losses
 
 
+def train(to_static, enable_prim, enable_cinn):
+    if core.is_compiled_with_cuda():
+        paddle.set_device('gpu')
+    else:
+        paddle.set_device('cpu')
+    np.random.seed(SEED)
+    paddle.seed(SEED)
+    paddle.framework.random._manual_program_seed(SEED)
+    fluid.core._set_prim_all_enabled(enable_prim)
+
+    train_reader = paddle.batch(
+        reader_decorator(paddle.dataset.flowers.train(use_xmap=False)),
+        batch_size=batch_size,
+        drop_last=True,
+    )
+    data_loader = fluid.io.DataLoader.from_generator(capacity=5, iterable=True)
+    data_loader.set_sample_list_generator(train_reader)
+
+    resnet = resnet50(False)
+    if to_static:
+        build_strategy = paddle.static.BuildStrategy()
+        if enable_cinn:
+            build_strategy.build_cinn_pass = True
+        resnet = paddle.jit.to_static(resnet, build_strategy=build_strategy)
+    optimizer = optimizer_setting(parameter_list=resnet.parameters())
+
+    train_losses = run(resnet, data_loader, optimizer, 'train')
+    if to_static and enable_prim and enable_cinn:
+        eval_losses = run(resnet, data_loader, optimizer, 'eval')
+    return train_losses
+
+
 class TestResnet(unittest.TestCase):
     @unittest.skipIf(
         not (paddle.is_compiled_with_cinn() and paddle.is_compiled_with_cuda()),