From 023d877152ad366e9d8ad73aaf28764b4b049b15 Mon Sep 17 00:00:00 2001 From: Weilong Wu Date: Mon, 28 Mar 2022 12:08:31 +0800 Subject: [PATCH] Update ResNet test cases (#40953) --- .../unittests/test_imperative_double_grad.py | 74 ++++++++++++++----- 1 file changed, 56 insertions(+), 18 deletions(-) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_double_grad.py b/python/paddle/fluid/tests/unittests/test_imperative_double_grad.py index bd7a333bba..9977756f40 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_double_grad.py +++ b/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__': -- GitLab