Update ResNet test cases (#40953)

python/paddle/fluid/tests/unittests/test_imperative_double_grad.py

@@ -20,7 +20,7 @@ import unittest
 from unittest import TestCase
 import numpy as np
 import paddle.compat as cpt
-from paddle.fluid.framework import _test_eager_guard, _in_legacy_dygraph
+from paddle.fluid.framework import _test_eager_guard, _in_legacy_dygraph, _in_eager_without_dygraph_check
 import paddle.fluid.core as core
 
 
@@ -568,13 +568,32 @@ class TestRaiseNoDoubleGradOp(TestCase):
         self.assertRaises(RuntimeError, self.raise_no_grad_op)
 
 
-class TestDoubleGradResNetBase(TestCase):
+class TestDoubleGradResNet(TestCase):
+    def setUp(self):
+        paddle.seed(123)
+        paddle.framework.random._manual_program_seed(123)
+        self.data = np.random.rand(1, 3, 224, 224).astype(np.float32)
+
     @dygraph_guard
-    def check_resnet(self):
-        data = np.random.rand(1, 3, 224, 224).astype(np.float32)
-        data = paddle.to_tensor(data)
+    def test_resnet_resnet50(self):
+        with _test_eager_guard():
+            model = resnet50(pretrained=False)
+            egr_data = paddle.to_tensor(self.data)
+            egr_data.stop_gradient = False
+            egr_out = model(egr_data)
+            egr_preds = paddle.argmax(egr_out, axis=1)
+            egr_label_onehot = paddle.nn.functional.one_hot(
+                paddle.to_tensor(egr_preds), num_classes=egr_out.shape[1])
+            egr_target = paddle.sum(egr_out * egr_label_onehot, axis=1)
+
+            egr_g = paddle.grad(outputs=egr_target, inputs=egr_out)[0]
+            egr_g_numpy = egr_g.numpy()
+            self.assertEqual(list(egr_g_numpy.shape), list(egr_out.shape))
+
+        model = resnet50(pretrained=False)
+        data = paddle.to_tensor(self.data)
         data.stop_gradient = False
-        out = self.model(data)
+        out = model(data)
         preds = paddle.argmax(out, axis=1)
         label_onehot = paddle.nn.functional.one_hot(
             paddle.to_tensor(preds), num_classes=out.shape[1])
@@ -584,21 +603,40 @@ class TestDoubleGradResNetBase(TestCase):
         g_numpy = g.numpy()
         self.assertEqual(list(g_numpy.shape), list(out.shape))
 
+        self.assertTrue(np.array_equal(egr_out, out))
+        self.assertTrue(np.array_equal(egr_g_numpy, g_numpy))
 
-class TestDoubleGradResNet50(TestDoubleGradResNetBase):
-    def setUp(self):
-        self.model = resnet50(pretrained=False)
-
-    def test_main(self):
-        self.check_resnet()
-
+    @dygraph_guard
+    def test_resnet_resnet101(self):
+        with _test_eager_guard():
+            model = resnet101(pretrained=False)
+            egr_data = paddle.to_tensor(self.data)
+            egr_data.stop_gradient = False
+            egr_out = model(egr_data)
+            egr_preds = paddle.argmax(egr_out, axis=1)
+            egr_label_onehot = paddle.nn.functional.one_hot(
+                paddle.to_tensor(egr_preds), num_classes=egr_out.shape[1])
+            egr_target = paddle.sum(egr_out * egr_label_onehot, axis=1)
+
+            egr_g = paddle.grad(outputs=egr_target, inputs=egr_out)[0]
+            egr_g_numpy = egr_g.numpy()
+            self.assertEqual(list(egr_g_numpy.shape), list(egr_out.shape))
+
+        model = resnet101(pretrained=False)
+        data = paddle.to_tensor(self.data)
+        data.stop_gradient = False
+        out = model(data)
+        preds = paddle.argmax(out, axis=1)
+        label_onehot = paddle.nn.functional.one_hot(
+            paddle.to_tensor(preds), num_classes=out.shape[1])
+        target = paddle.sum(out * label_onehot, axis=1)
 
-class TestDoubleGradResNet101(TestDoubleGradResNetBase):
-    def setUp(self):
-        self.model = resnet101(pretrained=False)
+        g = paddle.grad(outputs=target, inputs=out)[0]
+        g_numpy = g.numpy()
+        self.assertEqual(list(g_numpy.shape), list(out.shape))
 
-    def test_main(self):
-        self.check_resnet()
+        self.assertTrue(np.array_equal(egr_out, out))
+        self.assertTrue(np.array_equal(egr_g_numpy, g_numpy))
 
 
 if __name__ == '__main__':