diff --git a/python/paddle/fluid/tests/unittests/test_layers.py b/python/paddle/fluid/tests/unittests/test_layers.py index 04471495d453229419eb445f1569861019217909..81c36de5bbbb998d41f143ef43538990beb728f3 100644 --- a/python/paddle/fluid/tests/unittests/test_layers.py +++ b/python/paddle/fluid/tests/unittests/test_layers.py @@ -27,12 +27,7 @@ import paddle.fluid.nets as nets import paddle.nn.functional as F from paddle.fluid import core from paddle.fluid.dygraph import base, to_variable -from paddle.fluid.framework import ( - Program, - _test_eager_guard, - default_main_program, - program_guard, -) +from paddle.fluid.framework import Program, default_main_program, program_guard from paddle.tensor import random @@ -102,14 +97,6 @@ class TestLayer(LayerTest): return ret with self.dynamic_graph(): - with _test_eager_guard(): - inp = np.ones([3, 3], dtype='float32') - x = base.to_variable(inp) - custom = CustomLayer(input_size=3, linear1_size=2) - ret = custom(x, do_linear2=False) - np.testing.assert_array_equal(ret.numpy().shape, [3, 2]) - ret = custom(x, do_linear2=True) - np.testing.assert_array_equal(ret.numpy().shape, [3, 1]) inp = np.ones([3, 3], dtype='float32') x = base.to_variable(inp) custom = CustomLayer(input_size=3, linear1_size=2) @@ -134,14 +121,6 @@ class TestLayer(LayerTest): feed={'data': inp}, fetch_list=[ret, ret2] ) with self.dynamic_graph(): - with _test_eager_guard(): - t = base.to_variable(inp) - dropout = paddle.nn.Dropout(p=0.35) - dy_eager_ret = dropout(t) - dy_eager_ret2 = paddle.nn.functional.dropout(t, p=0.35) - dy_eager_ret_value = dy_eager_ret.numpy() - dy_eager_ret2_value = dy_eager_ret2.numpy() - t = base.to_variable(inp) dropout = paddle.nn.Dropout(p=0.35) dy_ret = dropout(t) @@ -149,9 +128,6 @@ class TestLayer(LayerTest): dy_ret_value = dy_ret.numpy() dy_ret2_value = dy_ret2.numpy() - np.testing.assert_array_equal(dy_eager_ret_value, dy_eager_ret2_value) - np.testing.assert_array_equal(static_ret, dy_eager_ret_value) - np.testing.assert_array_equal(static_ret, static_ret2) np.testing.assert_array_equal(dy_ret_value, dy_ret2_value) np.testing.assert_array_equal(static_ret, dy_ret_value) @@ -173,16 +149,6 @@ class TestLayer(LayerTest): feed={'data': inp}, fetch_list=[ret] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - t = base.to_variable(inp) - linear = paddle.nn.Linear( - 32, - 4, - bias_attr=fluid.initializer.ConstantInitializer(value=1), - ) - dy_eager_ret = linear(t) - dy_eager_ret_value = dy_eager_ret.numpy() - t = base.to_variable(inp) linear = paddle.nn.Linear( 32, 4, bias_attr=fluid.initializer.ConstantInitializer(value=1) @@ -190,7 +156,6 @@ class TestLayer(LayerTest): dy_ret = linear(t) dy_ret_value = dy_ret.numpy() - np.testing.assert_array_equal(static_ret, dy_eager_ret_value) np.testing.assert_array_equal(static_ret, dy_ret_value) with self.static_graph(): @@ -275,18 +240,11 @@ class TestLayer(LayerTest): feed={'data': inp}, fetch_list=[ret] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - t = base.to_variable(inp) - flatten = paddle.nn.Flatten() - dy_eager_ret = flatten(t) - dy_eager_ret_value = dy_eager_ret.numpy() - t = base.to_variable(inp) flatten = paddle.nn.Flatten() dy_ret = flatten(t) dy_ret_value = dy_ret.numpy() - np.testing.assert_array_equal(static_ret, dy_eager_ret_value) np.testing.assert_array_equal(static_ret, dy_ret_value) with self.static_graph(): @@ -328,18 +286,11 @@ class TestLayer(LayerTest): )[0] with self.dynamic_graph(): - with _test_eager_guard(): - t = np.ones([3, 3, 5, 5], dtype='float32') - my_syncbn = paddle.nn.SyncBatchNorm(3) - dy_eager_ret = my_syncbn(base.to_variable(t)) - dy_eager_ret_value = dy_eager_ret.numpy() - t = np.ones([3, 3, 5, 5], dtype='float32') my_syncbn = paddle.nn.SyncBatchNorm(3) dy_ret = my_syncbn(base.to_variable(t)) dy_ret_value = dy_ret.numpy() np.testing.assert_array_equal(static_ret, dy_ret_value) - np.testing.assert_array_equal(static_ret, dy_eager_ret_value) def test_relu(self): with self.static_graph(): @@ -350,17 +301,11 @@ class TestLayer(LayerTest): )[0] with self.dynamic_graph(): - with _test_eager_guard(): - t = np.ones([3, 3], dtype='float32') - dy_eager_ret = F.relu(base.to_variable(t)) - dy_eager_ret_value = dy_eager_ret.numpy() - t = np.ones([3, 3], dtype='float32') dy_ret = F.relu(base.to_variable(t)) dy_ret_value = dy_ret.numpy() np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05) - np.testing.assert_allclose(static_ret, dy_eager_ret_value, rtol=1e-05) def test_matmul(self): with self.static_graph(): @@ -376,21 +321,12 @@ class TestLayer(LayerTest): )[0] with self.dynamic_graph(): - with _test_eager_guard(): - t = np.ones([3, 3], dtype='float32') - t2 = np.ones([3, 3], dtype='float32') - dy_eager_ret = paddle.matmul( - base.to_variable(t), base.to_variable(t2) - ) - dy_eager_ret_value = dy_eager_ret.numpy() - t = np.ones([3, 3], dtype='float32') t2 = np.ones([3, 3], dtype='float32') dy_ret = paddle.matmul(base.to_variable(t), base.to_variable(t2)) dy_ret_value = dy_ret.numpy() np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05) - np.testing.assert_allclose(static_ret, dy_eager_ret_value, rtol=1e-05) def test_elementwise_math(self): n = np.ones([3, 3], dtype='float32') @@ -420,14 +356,6 @@ class TestLayer(LayerTest): )[0] with self.dynamic_graph(): - with _test_eager_guard(): - ret = paddle.add(to_variable(n), to_variable(n2)) - ret = paddle.pow(ret, to_variable(n3)) - ret = paddle.divide(ret, to_variable(n4)) - ret = paddle.subtract(ret, to_variable(n5)) - dy_eager_ret = paddle.multiply(ret, to_variable(n6)) - dy_eager_ret_value = dy_eager_ret.numpy() - ret = paddle.add(to_variable(n), to_variable(n2)) ret = paddle.pow(ret, to_variable(n3)) ret = paddle.divide(ret, to_variable(n4)) @@ -436,19 +364,12 @@ class TestLayer(LayerTest): dy_ret_value = dy_ret.numpy() np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05) - np.testing.assert_allclose(static_ret, dy_eager_ret_value, rtol=1e-05) def test_elementwise_minmax(self): n = np.ones([3, 3], dtype='float32') n2 = np.ones([3, 3], dtype='float32') * 2 with self.dynamic_graph(): - with _test_eager_guard(): - min_eager_ret = paddle.minimum(to_variable(n), to_variable(n2)) - max_eager_ret = paddle.maximum(to_variable(n), to_variable(n2)) - min_eager_ret_value = min_eager_ret.numpy() - max_eager_ret_value = max_eager_ret.numpy() - min_ret = paddle.minimum(to_variable(n), to_variable(n2)) max_ret = paddle.maximum(to_variable(n), to_variable(n2)) min_ret_value = min_ret.numpy() @@ -456,8 +377,6 @@ class TestLayer(LayerTest): np.testing.assert_allclose(n, min_ret_value, rtol=1e-05) np.testing.assert_allclose(n2, max_ret_value, rtol=1e-05) - np.testing.assert_allclose(n, min_eager_ret_value, rtol=1e-05) - np.testing.assert_allclose(n2, max_eager_ret_value, rtol=1e-05) def test_conv2d_transpose(self): inp_np = np.arange(0, 24).reshape([2, 3, 2, 2]).astype('float32') @@ -487,17 +406,6 @@ class TestLayer(LayerTest): feed={'pixel': inp_np}, fetch_list=[out] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - conv2d_transpose = paddle.nn.Conv2DTranspose( - 3, - 10, - 27, - bias_attr=fluid.initializer.ConstantInitializer(value=1), - ) - dy_eager_rlt = conv2d_transpose(base.to_variable(inp_np)) - dy_eager_rlt = paddle.nn.functional.sigmoid(dy_eager_rlt) - dy_eager_rlt_value = dy_eager_rlt.numpy() - conv2d_transpose = paddle.nn.Conv2DTranspose( 3, 10, @@ -509,53 +417,8 @@ class TestLayer(LayerTest): dy_rlt_value = dy_rlt.numpy() np.testing.assert_allclose(static_rlt2, static_rlt, rtol=1e-05) np.testing.assert_allclose(dy_rlt_value, static_rlt2, rtol=1e-05) - np.testing.assert_allclose(dy_eager_rlt_value, static_rlt2, rtol=1e-05) with self.dynamic_graph(): - with _test_eager_guard(): - images = np.ones([2, 3, 5, 5], dtype='float32') - custom_weight = np.random.randn(3, 3, 2, 2).astype("float32") - weight_attr = fluid.ParamAttr( - initializer=fluid.initializer.NumpyArrayInitializer( - custom_weight - ) - ) - conv2d1 = paddle.nn.Conv2DTranspose(3, 3, [2, 2]) - conv2d2 = paddle.nn.Conv2DTranspose( - 3, - 3, - [2, 2], - weight_attr=weight_attr, - ) - dy_ret1 = conv2d1(base.to_variable(images)) - dy_ret2 = conv2d2(base.to_variable(images)) - self.assertFalse( - np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()) - ) - - conv2d1_weight_np = conv2d1.weight.numpy() - conv2d1_bias = conv2d1.bias - self.assertFalse( - np.array_equal(conv2d1_weight_np, conv2d2.weight.numpy()) - ) - conv2d2.weight.set_value(conv2d1_weight_np) - np.testing.assert_array_equal( - conv2d1_weight_np, conv2d2.weight.numpy() - ) - conv2d2.bias.set_value(conv2d1_bias) - dy_ret1 = conv2d1(base.to_variable(images)) - dy_ret2 = conv2d2(base.to_variable(images)) - np.testing.assert_array_equal(dy_ret1.numpy(), dy_ret2.numpy()) - - conv2d2.weight = conv2d1.weight - conv2d2.bias = conv2d1.bias - np.testing.assert_array_equal( - conv2d1.weight.numpy(), conv2d2.weight.numpy() - ) - np.testing.assert_array_equal( - conv2d1.bias.numpy(), conv2d2.bias.numpy() - ) - images = np.ones([2, 3, 5, 5], dtype='float32') custom_weight = np.random.randn(3, 3, 2, 2).astype("float32") weight_attr = fluid.ParamAttr( @@ -660,19 +523,6 @@ class TestLayer(LayerTest): feed={'x': inp_np_x, 'y': inp_np_y}, fetch_list=[out] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - btp = paddle.nn.Bilinear( - 3, - 3, - 6, - bias_attr=fluid.initializer.ConstantInitializer(value=1), - ) - dy_eager_rlt = btp( - base.to_variable(inp_np_x), base.to_variable(inp_np_y) - ) - dy_eager_rlt = paddle.nn.functional.sigmoid(dy_eager_rlt) - dy_eager_rlt_value = dy_eager_rlt.numpy() - btp = paddle.nn.Bilinear( 3, 3, @@ -684,14 +534,6 @@ class TestLayer(LayerTest): dy_rlt_value = dy_rlt.numpy() with self.dynamic_graph(): - with _test_eager_guard(): - btp2 = paddle.nn.Bilinear(3, 3, 6) - dy_eager_rlt2 = btp2( - base.to_variable(inp_np_x), base.to_variable(inp_np_y) - ) - dy_eager_rlt2 = paddle.nn.functional.sigmoid(dy_eager_rlt2) - dy_eager_rlt2_value = dy_eager_rlt2.numpy() - btp2 = paddle.nn.Bilinear(3, 3, 6) dy_rlt2 = btp2( base.to_variable(inp_np_x), base.to_variable(inp_np_y) @@ -715,51 +557,10 @@ class TestLayer(LayerTest): )[0] np.testing.assert_array_equal(dy_rlt2_value, static_rlt3) - np.testing.assert_array_equal(dy_eager_rlt2_value, static_rlt3) np.testing.assert_array_equal(static_rlt2, static_rlt) np.testing.assert_array_equal(dy_rlt_value, static_rlt) - np.testing.assert_array_equal(dy_eager_rlt_value, static_rlt) with self.dynamic_graph(): - with _test_eager_guard(): - custom_weight = np.random.randn(6, 3, 3).astype("float32") - weight_attr = fluid.ParamAttr( - initializer=fluid.initializer.NumpyArrayInitializer( - custom_weight - ) - ) - btp1 = paddle.nn.Bilinear(3, 3, 6) - btp2 = paddle.nn.Bilinear(3, 3, 6, weight_attr=weight_attr) - dy_rlt1 = btp1( - base.to_variable(inp_np_x), base.to_variable(inp_np_y) - ) - dy_rlt1 = paddle.nn.functional.sigmoid(dy_rlt1) - dy_rlt2 = btp2( - base.to_variable(inp_np_x), base.to_variable(inp_np_y) - ) - dy_rlt2 = paddle.nn.functional.sigmoid(dy_rlt2) - self.assertFalse( - np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy()) - ) - btp2.weight.set_value(btp1.weight.numpy()) - btp2.bias.set_value(btp1.bias) - dy_rlt1 = btp1( - base.to_variable(inp_np_x), base.to_variable(inp_np_y) - ) - dy_rlt2 = btp2( - base.to_variable(inp_np_x), base.to_variable(inp_np_y) - ) - np.testing.assert_array_equal(dy_rlt1.numpy(), dy_rlt2.numpy()) - - btp2.weight = btp1.weight - btp2.bias = btp1.bias - np.testing.assert_array_equal( - btp1.weight.numpy(), btp2.weight.numpy() - ) - np.testing.assert_array_equal( - btp1.bias.numpy(), btp2.bias.numpy() - ) - custom_weight = np.random.randn(6, 3, 3).astype("float32") weight_attr = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( @@ -818,15 +619,6 @@ class TestLayer(LayerTest): feed={'word': inp_word}, fetch_list=[emb_rlt] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - emb2 = paddle.nn.Embedding( - dict_size, - 32, - weight_attr='eager_emb.w', - sparse=False, - ) - dy_eager_rlt = emb2(base.to_variable(inp_word)) - dy_eager_rlt_value = dy_eager_rlt.numpy() emb2 = paddle.nn.Embedding( dict_size, 32, weight_attr='emb.w', sparse=False @@ -836,41 +628,8 @@ class TestLayer(LayerTest): self.assertTrue(np.allclose(static_rlt2, static_rlt)) self.assertTrue(np.allclose(dy_rlt_value, static_rlt)) - self.assertTrue(np.allclose(dy_eager_rlt_value, static_rlt)) with self.dynamic_graph(): - with _test_eager_guard(): - custom_weight = np.random.randn(dict_size, 32).astype("float32") - weight_attr = fluid.ParamAttr( - initializer=fluid.initializer.NumpyArrayInitializer( - custom_weight - ) - ) - emb1 = paddle.nn.Embedding(dict_size, 32, sparse=False) - emb2 = paddle.nn.Embedding( - dict_size, - 32, - weight_attr=weight_attr, - sparse=False, - ) - rep1 = emb1(base.to_variable(inp_word)) - rep2 = emb2(base.to_variable(inp_word)) - self.assertFalse( - np.array_equal(emb1.weight.numpy(), custom_weight) - ) - np.testing.assert_array_equal( - emb2.weight.numpy(), custom_weight - ) - self.assertFalse(np.array_equal(rep1.numpy(), rep2.numpy())) - emb2.weight.set_value(emb1.weight.numpy()) - rep2 = emb2(base.to_variable(inp_word)) - np.testing.assert_array_equal(rep1.numpy(), rep2.numpy()) - - emb2.weight = emb1.weight - np.testing.assert_array_equal( - emb1.weight.numpy(), emb2.weight.numpy() - ) - custom_weight = np.random.randn(dict_size, 32).astype("float32") weight_attr = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( @@ -897,18 +656,6 @@ class TestLayer(LayerTest): def test_one_hot(self): with self.dynamic_graph(): - with _test_eager_guard(): - label = fluid.dygraph.to_variable( - np.array([[1], [1], [3], [0]]) - ) - one_hot_label1 = fluid.layers.one_hot(input=label, depth=4) - one_hot_label2 = fluid.layers.one_hot( - input=label, depth=fluid.dygraph.to_variable(np.array([4])) - ) - np.testing.assert_array_equal( - one_hot_label1.numpy(), one_hot_label2.numpy() - ) - label = fluid.dygraph.to_variable(np.array([[1], [1], [3], [0]])) one_hot_label1 = fluid.layers.one_hot(input=label, depth=4) one_hot_label2 = fluid.layers.one_hot( @@ -920,17 +667,6 @@ class TestLayer(LayerTest): def test_split(self): with self.dynamic_graph(): - with _test_eager_guard(): - input = fluid.dygraph.to_variable(np.random.random((3, 8, 5))) - x0, x1 = paddle.split(input, num_or_sections=2, axis=1) - x00, x11 = paddle.split( - input, - num_or_sections=2, - axis=fluid.dygraph.to_variable(np.array([1])), - ) - np.testing.assert_array_equal(x0.numpy(), x00.numpy()) - np.testing.assert_array_equal(x1.numpy(), x11.numpy()) - input = fluid.dygraph.to_variable(np.random.random((3, 8, 5))) x0, x1 = paddle.split(input, num_or_sections=2, axis=1) x00, x11 = paddle.split( @@ -943,19 +679,6 @@ class TestLayer(LayerTest): def test_topk(self): with self.dynamic_graph(): - with _test_eager_guard(): - input = fluid.dygraph.to_variable(np.random.random((13, 11))) - top5_values1, top5_indices1 = paddle.topk(input, k=5) - top5_values2, top5_indices2 = paddle.topk( - input, k=fluid.dygraph.to_variable(np.array([5])) - ) - np.testing.assert_array_equal( - top5_values1.numpy(), top5_values2.numpy() - ) - np.testing.assert_array_equal( - top5_indices1.numpy(), top5_indices2.numpy() - ) - input = fluid.dygraph.to_variable(np.random.random((13, 11))) top5_values1, top5_indices1 = paddle.topk(input, k=5) top5_values2, top5_indices2 = paddle.topk( @@ -995,14 +718,6 @@ class TestLayer(LayerTest): )[0] with self.dynamic_graph(): - with _test_eager_guard(): - images = np.ones([2, 3, 6, 6, 6], dtype='float32') - conv3d = paddle.nn.Conv3D( - in_channels=3, out_channels=3, kernel_size=2 - ) - dy_eager_ret = conv3d(base.to_variable(images)) - dy_eager_rlt_value = dy_eager_ret.numpy() - images = np.ones([2, 3, 6, 6, 6], dtype='float32') conv3d = paddle.nn.Conv3D( in_channels=3, out_channels=3, kernel_size=2 @@ -1011,56 +726,9 @@ class TestLayer(LayerTest): dy_rlt_value = dy_ret.numpy() np.testing.assert_allclose(static_ret, dy_rlt_value, rtol=1e-05) - np.testing.assert_allclose(static_ret, dy_eager_rlt_value, rtol=1e-05) np.testing.assert_allclose(static_ret, static_ret2, rtol=1e-05) with self.dynamic_graph(): - with _test_eager_guard(): - images = np.ones([2, 3, 6, 6, 6], dtype='float32') - custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32") - weight_attr = fluid.ParamAttr( - initializer=fluid.initializer.NumpyArrayInitializer( - custom_weight - ) - ) - conv3d1 = paddle.nn.Conv3D( - in_channels=3, out_channels=3, kernel_size=2 - ) - conv3d2 = paddle.nn.Conv3D( - in_channels=3, - out_channels=3, - kernel_size=2, - weight_attr=weight_attr, - ) - dy_ret1 = conv3d1(base.to_variable(images)) - dy_ret2 = conv3d2(base.to_variable(images)) - self.assertFalse( - np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()) - ) - - conv3d1_weight_np = conv3d1.weight.numpy() - conv3d1_bias = conv3d1.bias - self.assertFalse( - np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy()) - ) - conv3d2.weight.set_value(conv3d1_weight_np) - np.testing.assert_array_equal( - conv3d1_weight_np, conv3d2.weight.numpy() - ) - conv3d1.bias.set_value(conv3d1_bias) - dy_ret1 = conv3d1(base.to_variable(images)) - dy_ret2 = conv3d2(base.to_variable(images)) - np.testing.assert_array_equal(dy_ret1.numpy(), dy_ret2.numpy()) - - conv3d2.weight = conv3d1.weight - conv3d2.bias = conv3d1.bias - np.testing.assert_array_equal( - conv3d1.weight.numpy(), conv3d2.weight.numpy() - ) - np.testing.assert_array_equal( - conv3d1.bias.numpy(), conv3d2.bias.numpy() - ) - images = np.ones([2, 3, 6, 6, 6], dtype='float32') custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32") weight_attr = fluid.ParamAttr( @@ -1104,7 +772,7 @@ class TestLayer(LayerTest): conv3d1.bias.numpy(), conv3d2.bias.numpy() ) - def func_group_norm(self): + def test_group_norm(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: @@ -1176,11 +844,6 @@ class TestLayer(LayerTest): np.testing.assert_allclose(static_ret, dy_rlt_value, rtol=1e-05) np.testing.assert_allclose(static_ret, static_ret2, rtol=1e-05) - def test_group_norm(self): - with _test_eager_guard(): - self.func_group_norm() - self.func_group_norm() - def test_instance_norm(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) @@ -1211,29 +874,17 @@ class TestLayer(LayerTest): )[0] with self.dynamic_graph(): - with _test_eager_guard(): - instanceNorm = paddle.nn.InstanceNorm2D(num_features=shape[1]) - dy_eager_ret = instanceNorm(base.to_variable(input)) - dy_eager_rlt_value = dy_eager_ret.numpy() - instanceNorm = paddle.nn.InstanceNorm2D(num_features=shape[1]) dy_ret = instanceNorm(base.to_variable(input)) dy_rlt_value = dy_ret.numpy() with self.dynamic_graph(): - with _test_eager_guard(): - instanceNorm = paddle.nn.InstanceNorm2D(num_features=shape[1]) - dy_eager_ret = instanceNorm(base.to_variable(input)) - dy_eager_rlt_value2 = dy_eager_ret.numpy() - instanceNorm = paddle.nn.InstanceNorm2D(num_features=shape[1]) dy_ret = instanceNorm(base.to_variable(input)) dy_rlt_value2 = dy_ret.numpy() np.testing.assert_allclose(static_ret, dy_rlt_value, rtol=1e-05) np.testing.assert_allclose(static_ret, dy_rlt_value2, rtol=1e-05) - np.testing.assert_allclose(static_ret, dy_eager_rlt_value, rtol=1e-05) - np.testing.assert_allclose(static_ret, dy_eager_rlt_value2, rtol=1e-05) np.testing.assert_allclose(static_ret, static_ret2, rtol=1e-05) with self.static_graph(): @@ -1302,19 +953,11 @@ class TestLayer(LayerTest): )[0] with self.dynamic_graph(): - with _test_eager_guard(): - spectralNorm = paddle.nn.SpectralNorm( - shape, axis=1, power_iters=2 - ) - dy_eager_ret = spectralNorm(base.to_variable(input)) - dy_eager_rlt_value = dy_eager_ret.numpy() - spectralNorm = paddle.nn.SpectralNorm(shape, axis=1, power_iters=2) dy_ret = spectralNorm(base.to_variable(input)) dy_rlt_value = dy_ret.numpy() np.testing.assert_allclose(static_ret, dy_rlt_value, rtol=1e-05) - np.testing.assert_allclose(static_ret, dy_eager_rlt_value, rtol=1e-05) np.testing.assert_allclose(static_ret, static_ret2, rtol=1e-05) def test_conv3d_transpose(self): @@ -1340,15 +983,6 @@ class TestLayer(LayerTest): feed={'pixel': input_array}, fetch_list=[out] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - conv3d_transpose = paddle.nn.Conv3DTranspose( - in_channels=3, - out_channels=12, - kernel_size=12, - ) - dy_eager_rlt = conv3d_transpose(base.to_variable(input_array)) - dy_eager_rlt_value = dy_eager_rlt.numpy() - conv3d_transpose = paddle.nn.Conv3DTranspose( in_channels=3, out_channels=12, kernel_size=12 ) @@ -1356,59 +990,8 @@ class TestLayer(LayerTest): dy_rlt_value = dy_rlt.numpy() np.testing.assert_allclose(static_rlt2, static_rlt, rtol=1e-05) np.testing.assert_allclose(dy_rlt_value, static_rlt, rtol=1e-05) - np.testing.assert_allclose(dy_eager_rlt_value, static_rlt, rtol=1e-05) with self.dynamic_graph(): - with _test_eager_guard(): - images = np.ones([2, 3, 6, 6, 6], dtype='float32') - custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32") - weight_attr = fluid.ParamAttr( - initializer=fluid.initializer.NumpyArrayInitializer( - custom_weight - ) - ) - conv3d1 = paddle.nn.Conv3DTranspose( - in_channels=3, - out_channels=3, - kernel_size=2, - bias_attr='eager_conv3d1_b', - ) - conv3d2 = paddle.nn.Conv3DTranspose( - in_channels=3, - out_channels=3, - kernel_size=2, - weight_attr=weight_attr, - bias_attr='eager_conv3d2_b', - ) - dy_ret1 = conv3d1(base.to_variable(images)) - dy_ret2 = conv3d2(base.to_variable(images)) - self.assertFalse( - np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()) - ) - - conv3d1_weight_np = conv3d1.weight.numpy() - conv3d1_bias = conv3d1.bias - self.assertFalse( - np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy()) - ) - conv3d2.weight.set_value(conv3d1_weight_np) - np.testing.assert_array_equal( - conv3d1_weight_np, conv3d2.weight.numpy() - ) - conv3d1.bias.set_value(conv3d1_bias) - dy_ret1 = conv3d1(base.to_variable(images)) - dy_ret2 = conv3d2(base.to_variable(images)) - np.testing.assert_array_equal(dy_ret1.numpy(), dy_ret2.numpy()) - - conv3d2.weight = conv3d1.weight - conv3d2.bias = conv3d1.bias - np.testing.assert_array_equal( - conv3d1.weight.numpy(), conv3d2.weight.numpy() - ) - np.testing.assert_array_equal( - conv3d1.bias.numpy(), conv3d2.bias.numpy() - ) - images = np.ones([2, 3, 6, 6, 6], dtype='float32') custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32") weight_attr = fluid.ParamAttr( @@ -1456,7 +1039,7 @@ class TestLayer(LayerTest): conv3d1.bias.numpy(), conv3d2.bias.numpy() ) - def func_while_loop(self): + def test_while_loop(self): with self.static_graph(): i = layers.fill_constant(shape=[1], dtype='int64', value=0) ten = layers.fill_constant(shape=[1], dtype='int64', value=10) @@ -1491,11 +1074,6 @@ class TestLayer(LayerTest): np.testing.assert_array_equal(static_ret[0], dy_ret[0].numpy()) - def test_while_loop(self): - with _test_eager_guard(): - self.func_while_loop() - self.func_while_loop() - def test_compare(self): value_a = np.arange(3) value_b = np.arange(3) @@ -1508,14 +1086,6 @@ class TestLayer(LayerTest): feed={"a": value_a, "b": value_b}, fetch_list=[cond] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - da = base.to_variable(value_a) - db = base.to_variable(value_b) - dcond = paddle.less_than(x=da, y=db) - - for i in range(len(static_ret)): - self.assertTrue(dcond.numpy()[i] == static_ret[i]) - da = base.to_variable(value_a) db = base.to_variable(value_b) dcond = paddle.less_than(x=da, y=db) @@ -1532,14 +1102,6 @@ class TestLayer(LayerTest): feed={"a1": value_a, "b1": value_b}, fetch_list=[cond1] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - da1 = base.to_variable(value_a) - db1 = base.to_variable(value_b) - dcond1 = paddle.less_equal(x=da1, y=db1) - - for i in range(len(static_ret1)): - self.assertTrue(dcond1.numpy()[i] == static_ret1[i]) - da1 = base.to_variable(value_a) db1 = base.to_variable(value_b) dcond1 = paddle.less_equal(x=da1, y=db1) @@ -1556,14 +1118,6 @@ class TestLayer(LayerTest): feed={"a2": value_a, "b2": value_b}, fetch_list=[cond2] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - da2 = base.to_variable(value_a) - db2 = base.to_variable(value_b) - dcond2 = paddle.greater_than(x=da2, y=db2) - - for i in range(len(static_ret2)): - self.assertTrue(dcond2.numpy()[i] == static_ret2[i]) - da2 = base.to_variable(value_a) db2 = base.to_variable(value_b) dcond2 = paddle.greater_than(x=da2, y=db2) @@ -1580,14 +1134,6 @@ class TestLayer(LayerTest): feed={"a3": value_a, "b3": value_b}, fetch_list=[cond3] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - da3 = base.to_variable(value_a) - db3 = base.to_variable(value_b) - dcond3 = paddle.greater_equal(x=da3, y=db3) - - for i in range(len(static_ret3)): - self.assertTrue(dcond3.numpy()[i] == static_ret3[i]) - da3 = base.to_variable(value_a) db3 = base.to_variable(value_b) dcond3 = paddle.greater_equal(x=da3, y=db3) @@ -1604,14 +1150,6 @@ class TestLayer(LayerTest): feed={"a4": value_a, "b4": value_b}, fetch_list=[cond4] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - da4 = base.to_variable(value_a) - db4 = base.to_variable(value_b) - dcond4 = paddle.equal(x=da4, y=db4) - - for i in range(len(static_ret4)): - self.assertTrue(dcond4.numpy()[i] == static_ret4[i]) - da4 = base.to_variable(value_a) db4 = base.to_variable(value_b) dcond4 = paddle.equal(x=da4, y=db4) @@ -1628,14 +1166,6 @@ class TestLayer(LayerTest): feed={"a5": value_a, "b5": value_b}, fetch_list=[cond5] )[0] with self.dynamic_graph(): - with _test_eager_guard(): - da5 = base.to_variable(value_a) - db5 = base.to_variable(value_b) - dcond5 = paddle.equal(x=da5, y=db5) - - for i in range(len(static_ret5)): - self.assertTrue(dcond5.numpy()[i] == static_ret5[i]) - da5 = base.to_variable(value_a) db5 = base.to_variable(value_b) dcond5 = paddle.equal(x=da5, y=db5) @@ -1672,31 +1202,6 @@ class TestLayer(LayerTest): static_res = ret[0] with self.dynamic_graph(): - with _test_eager_guard(): - a = fluid.dygraph.to_variable(np.array([0.1]).astype('float32')) - b = fluid.dygraph.to_variable( - np.array([0.23]).astype('float32') - ) - out = paddle.static.nn.cond( - a < b, - lambda: less_than_branch(a, b), - lambda: greater_equal_branch(a, b), - ) - out2 = paddle.static.nn.cond( - a >= b, - lambda: greater_equal_branch(a, b), - lambda: less_than_branch(a, b), - ) - eager_dynamic_res = out.numpy() - eager_dynamic_res2 = out2.numpy() - np.testing.assert_array_equal( - eager_dynamic_res, eager_dynamic_res2 - ) - with self.assertRaises(TypeError): - paddle.static.nn.cond(a < b, 'str', 'str') - with self.assertRaises(TypeError): - paddle.static.nn.cond(a >= b, 'str', 'str') - a = fluid.dygraph.to_variable(np.array([0.1]).astype('float32')) b = fluid.dygraph.to_variable(np.array([0.23]).astype('float32')) out = paddle.static.nn.cond( @@ -1718,7 +1223,6 @@ class TestLayer(LayerTest): paddle.static.nn.cond(a >= b, 'str', 'str') np.testing.assert_array_equal(static_res, dynamic_res) - np.testing.assert_array_equal(static_res, eager_dynamic_res) def test_case(self): def fn_1(): @@ -1755,24 +1259,6 @@ class TestLayer(LayerTest): static_res1, static_res2 = exe.run(fetch_list=[out_1, out_2]) with self.dynamic_graph(): - with _test_eager_guard(): - x = layers.fill_constant(shape=[1], dtype='float32', value=0.3) - y = layers.fill_constant(shape=[1], dtype='float32', value=0.1) - z = layers.fill_constant(shape=[1], dtype='float32', value=0.2) - - pred_1 = paddle.less_than(z, x) # true: 0.2 < 0.3 - pred_2 = paddle.less_than(x, y) # false: 0.3 < 0.1 - pred_3 = paddle.equal(x, y) # false: 0.3 == 0.1 - - out_1 = paddle.static.nn.case( - pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3 - ) - out_2 = paddle.static.nn.case( - pred_fn_pairs=[(pred_2, fn_2), (pred_3, fn_3)] - ) - eager_dynamic_res1 = out_1.numpy() - eager_dynamic_res2 = out_2.numpy() - x = layers.fill_constant(shape=[1], dtype='float32', value=0.3) y = layers.fill_constant(shape=[1], dtype='float32', value=0.1) z = layers.fill_constant(shape=[1], dtype='float32', value=0.2) @@ -1792,8 +1278,6 @@ class TestLayer(LayerTest): np.testing.assert_array_equal(static_res1, dynamic_res1) np.testing.assert_array_equal(static_res2, dynamic_res2) - np.testing.assert_array_equal(static_res1, eager_dynamic_res1) - np.testing.assert_array_equal(static_res2, eager_dynamic_res2) def test_switch_case(self): def fn_1(): @@ -1835,33 +1319,6 @@ class TestLayer(LayerTest): ) with self.dynamic_graph(): - with _test_eager_guard(): - index_1 = layers.fill_constant( - shape=[1], dtype='int32', value=1 - ) - index_2 = layers.fill_constant( - shape=[1], dtype='int32', value=2 - ) - - out_1 = paddle.static.nn.switch_case( - branch_index=index_1, - branch_fns={1: fn_1, 2: fn_2}, - default=fn_3, - ) - out_2 = paddle.static.nn.switch_case( - branch_index=index_2, - branch_fns=[(1, fn_1), (2, fn_2)], - default=fn_3, - ) - out_3 = paddle.static.nn.switch_case( - branch_index=index_2, - branch_fns=[(0, fn_1), (4, fn_2), (7, fn_3)], - ) - - eager_dynamic_res1 = out_1.numpy() - eager_dynamic_res2 = out_2.numpy() - eager_dynamic_res3 = out_3.numpy() - index_1 = layers.fill_constant(shape=[1], dtype='int32', value=1) index_2 = layers.fill_constant(shape=[1], dtype='int32', value=2) @@ -1887,9 +1344,6 @@ class TestLayer(LayerTest): np.testing.assert_array_equal(static_res1, dynamic_res1) np.testing.assert_array_equal(static_res2, dynamic_res2) np.testing.assert_array_equal(static_res3, dynamic_res3) - np.testing.assert_array_equal(static_res1, eager_dynamic_res1) - np.testing.assert_array_equal(static_res2, eager_dynamic_res2) - np.testing.assert_array_equal(static_res3, eager_dynamic_res3) def test_crop_tensor(self): with self.static_graph(): @@ -1972,7 +1426,7 @@ class TestBook(LayerTest): ) self.all_close_compare = set({"make_spectral_norm"}) - def func_all_layers(self): + def test_all_layers(self): attrs = (getattr(self, name) for name in dir(self)) methods = filter(inspect.ismethod, attrs) for method in methods: @@ -2028,11 +1482,6 @@ class TestBook(LayerTest): ), ) - def test_all_layers(self): - with _test_eager_guard(): - self.func_all_layers() - self.func_all_layers() - def _get_np_data(self, shape, dtype, append_batch_size=True): np.random.seed(self.seed) if append_batch_size: diff --git a/python/paddle/fluid/tests/unittests/test_limit_by_capacity_op.py b/python/paddle/fluid/tests/unittests/test_limit_by_capacity_op.py index 597ffcf79714b4385b560dd9983ecc8a04497ea9..00fbf9b8dc68a7aeb36eca8273f3e3b10b0c3950 100644 --- a/python/paddle/fluid/tests/unittests/test_limit_by_capacity_op.py +++ b/python/paddle/fluid/tests/unittests/test_limit_by_capacity_op.py @@ -19,7 +19,6 @@ import numpy as np import paddle from paddle.distributed.models.moe import utils from paddle.fluid import core -from paddle.fluid.framework import _test_eager_guard def limit_by_capacity(expert_count, _capacity, n_worker): @@ -88,7 +87,7 @@ class TestLimitByCapacityInt64API(unittest.TestCase): assert all_close(self.out, res[0], self.n_worker) - def func_dygraph_api(self): + def test_dygraph_api(self): paddle.disable_static(self.place) capacity = paddle.to_tensor(self.capacity) expert_count_tensor = paddle.to_tensor(self.expert_count) @@ -97,11 +96,6 @@ class TestLimitByCapacityInt64API(unittest.TestCase): ) assert all_close(self.out, out.numpy(), self.n_worker) - def test_dygraph_api(self): - with _test_eager_guard(): - self.func_dygraph_api() - self.func_dygraph_api() - @unittest.skipIf( not core.is_compiled_with_cuda(), "core is not compiled with CUDA" diff --git a/python/paddle/fluid/tests/unittests/test_linalg_cond.py b/python/paddle/fluid/tests/unittests/test_linalg_cond.py index 61ad09cbfe400b78a43fea44dc3c4d62b3f4238b..68b4287f2f6ec5446b5cded51d81042339b2e456 100644 --- a/python/paddle/fluid/tests/unittests/test_linalg_cond.py +++ b/python/paddle/fluid/tests/unittests/test_linalg_cond.py @@ -18,7 +18,6 @@ import numpy as np import paddle import paddle.static as static -from paddle.fluid.framework import _test_eager_guard p_list_n_n = ("fro", "nuc", 1, -1, np.inf, -np.inf) p_list_m_n = (None, 2, -2) @@ -92,21 +91,16 @@ class API_TestStaticCond(unittest.TestCase): class API_TestDygraphCond(unittest.TestCase): - def func_out(self): + def test_out(self): paddle.disable_static() # test calling results of 'cond' in dynamic mode x_list_n_n, x_list_m_n = gen_input() test_dygraph_assert_true(self, x_list_n_n, p_list_n_n + p_list_m_n) test_dygraph_assert_true(self, x_list_m_n, p_list_m_n) - def test_out(self): - with _test_eager_guard(): - self.func_out() - self.func_out() - class TestCondAPIError(unittest.TestCase): - def func_dygraph_api_error(self): + def test_dygraph_api_error(self): paddle.disable_static() # test raising errors when 'cond' is called in dygraph mode p_list_error = ('fro_', '_nuc', -0.7, 0, 1.5, 3) @@ -121,11 +115,6 @@ class TestCondAPIError(unittest.TestCase): x_tensor = paddle.to_tensor(x) self.assertRaises(ValueError, paddle.linalg.cond, x_tensor, p) - def test_dygraph_api_error(self): - with _test_eager_guard(): - self.func_dygraph_api_error() - self.func_dygraph_api_error() - def test_static_api_error(self): paddle.enable_static() # test raising errors when 'cond' is called in static mode @@ -162,18 +151,13 @@ class TestCondAPIError(unittest.TestCase): class TestCondEmptyTensorInput(unittest.TestCase): - def func_dygraph_empty_tensor_input(self): + def test_dygraph_empty_tensor_input(self): paddle.disable_static() # test calling results of 'cond' when input is an empty tensor in dynamic mode x_list_n_n, x_list_m_n = gen_empty_input() test_dygraph_assert_true(self, x_list_n_n, p_list_n_n + p_list_m_n) test_dygraph_assert_true(self, x_list_m_n, p_list_m_n) - def test_dygraph_empty_tensor_input(self): - with _test_eager_guard(): - self.func_dygraph_empty_tensor_input() - self.func_dygraph_empty_tensor_input() - if __name__ == "__main__": paddle.enable_static() diff --git a/python/paddle/fluid/tests/unittests/test_linspace.py b/python/paddle/fluid/tests/unittests/test_linspace.py index 5905f617d169263fd829ae63b332e4de8048004a..27020dd2e0c237469eb5469dbe67f4ac2fd504fb 100644 --- a/python/paddle/fluid/tests/unittests/test_linspace.py +++ b/python/paddle/fluid/tests/unittests/test_linspace.py @@ -20,7 +20,6 @@ from op_test import OpTest import paddle import paddle.fluid as fluid from paddle.fluid import Program, core, program_guard -from paddle.fluid.framework import _test_eager_guard class TestLinspaceOpCommonCase(OpTest): @@ -128,11 +127,6 @@ class TestLinspaceAPI(unittest.TestCase): self.assertEqual((out2.numpy() == np_out2).all(), True) self.assertEqual((out3.numpy() == np_out3).all(), True) - def test_api_eager_dygraph(self): - with _test_eager_guard(): - self.test_variable_input2() - self.test_imperative() - class TestLinspaceOpError(unittest.TestCase): def test_errors(self): diff --git a/python/paddle/fluid/tests/unittests/test_logical_op.py b/python/paddle/fluid/tests/unittests/test_logical_op.py index 02466986238c103f7df78477d699bebb9a921cd8..cccee4e8bc8f63f3c8d56bea6691019fd0196fac 100755 --- a/python/paddle/fluid/tests/unittests/test_logical_op.py +++ b/python/paddle/fluid/tests/unittests/test_logical_op.py @@ -17,7 +17,6 @@ import unittest import numpy as np import paddle -from paddle.fluid.framework import _test_eager_guard from paddle.framework import _non_static_mode from paddle.static import Executor, Program, program_guard @@ -106,15 +105,14 @@ def run_eager(x_np, y_np, op_str, use_gpu=False, binary_op=True): if use_gpu and paddle.is_compiled_with_cuda(): place = paddle.CUDAPlace(0) paddle.disable_static(place) - with _test_eager_guard(): - op = getattr(paddle, op_str) - x = paddle.to_tensor(x_np, dtype=x_np.dtype) - if not binary_op: - dygraph_result = op(x) - else: - y = paddle.to_tensor(y_np, dtype=y_np.dtype) - dygraph_result = op(x, y) - return dygraph_result + op = getattr(paddle, op_str) + x = paddle.to_tensor(x_np, dtype=x_np.dtype) + if not binary_op: + dygraph_result = op(x) + else: + y = paddle.to_tensor(y_np, dtype=y_np.dtype) + dygraph_result = op(x, y) + return dygraph_result def np_data_generator(np_shape, dtype, *args, **kwargs): diff --git a/python/paddle/fluid/tests/unittests/test_logit_op.py b/python/paddle/fluid/tests/unittests/test_logit_op.py index 464247d3e73ab048ffdd5dabef67c7c665d233b7..0744b779fb481debfcccbfa711bd9b68e2867770 100644 --- a/python/paddle/fluid/tests/unittests/test_logit_op.py +++ b/python/paddle/fluid/tests/unittests/test_logit_op.py @@ -18,7 +18,6 @@ import numpy as np from op_test import OpTest import paddle -from paddle.fluid.framework import _test_eager_guard np.random.seed(10) @@ -117,11 +116,6 @@ class TestLogitAPI(unittest.TestCase): x = paddle.fluid.data(name='X2', shape=[100], dtype='float32') self.assertRaises(TypeError, paddle.logit, x, dtype='int32') - def test_api_eager_dygraph(self): - with _test_eager_guard(): - self.test_check_api() - self.test_errors() - if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_lookahead.py b/python/paddle/fluid/tests/unittests/test_lookahead.py index e90c9bf0c8b629667b656b0968d354e9748146c3..4fa8666d4b1f94f4116b65a5ceb93e257992e190 100644 --- a/python/paddle/fluid/tests/unittests/test_lookahead.py +++ b/python/paddle/fluid/tests/unittests/test_lookahead.py @@ -19,7 +19,6 @@ import numpy as np import paddle import paddle.fluid as fluid import paddle.nn as nn -from paddle.fluid.framework import _test_eager_guard LOOKAHEAD_K = 5 LOOKAHEAD_ALPHA = 0.2 @@ -71,7 +70,7 @@ class TestLookAhead(unittest.TestCase): ) fast_param = latest_b - SGD_LR * b_grad - def func_test_look_ahead_dygraph(self): + def test_look_ahead_dygraph(self): BATCH_SIZE = 16 BATCH_NUM = 4 EPOCH_NUM = 4 @@ -152,11 +151,6 @@ class TestLookAhead(unittest.TestCase): train(layer, loader, loss_fn, lookahead) - def test_look_ahead_dygraph(self): - with _test_eager_guard(): - self.func_test_look_ahead_dygraph() - self.func_test_look_ahead_dygraph() - if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_matmul_v2_op.py b/python/paddle/fluid/tests/unittests/test_matmul_v2_op.py index c452958ead8414fcac8cb21b750a01bfc58a1ad9..8136425595a1cfad43b77fb9f9d12d9093a86e26 100644 --- a/python/paddle/fluid/tests/unittests/test_matmul_v2_op.py +++ b/python/paddle/fluid/tests/unittests/test_matmul_v2_op.py @@ -20,7 +20,6 @@ from op_test import OpTest, convert_float_to_uint16, get_numeric_gradient import paddle import paddle.fluid as fluid import paddle.fluid.core as core -from paddle.fluid.framework import _test_eager_guard from paddle.fluid.tests.unittests.testsuite import create_op @@ -559,11 +558,6 @@ class TestMatMulV2API(unittest.TestCase): {'FLAGS_gemm_use_half_precision_compute_type': False} ) - def test_api_eager_dygraph(self): - with _test_eager_guard(): - self.test_dygraph() - self.test_dygraph_fp16() - class TestComplexMatMulOp(OpTest): def setUp(self): @@ -732,10 +726,6 @@ class TestMatmulop(unittest.TestCase): paddle.enable_static() - def func_dygraph_matmul(self): # noqa: F811 - with _test_eager_guard(): - self.func_dygraph_matmul() - if __name__ == "__main__": paddle.enable_static() diff --git a/python/paddle/fluid/tests/unittests/test_max_op.py b/python/paddle/fluid/tests/unittests/test_max_op.py index 9cac427d76f3ea7946e8477c1764e40c9c07d47d..b02fa4f9fb35072681b5c9825d85c58ff4027efe 100644 --- a/python/paddle/fluid/tests/unittests/test_max_op.py +++ b/python/paddle/fluid/tests/unittests/test_max_op.py @@ -20,7 +20,6 @@ from test_sum_op import TestReduceOPTensorAxisBase import paddle import paddle.fluid.core as core -from paddle.fluid.framework import _test_eager_guard class ApiMaxTest(unittest.TestCase): @@ -83,10 +82,6 @@ class ApiMaxTest(unittest.TestCase): z_expected = np.array(np.max(np_x, axis=0)) self.assertEqual((np_z == z_expected).all(), True) - def test_eager_api(self): - with _test_eager_guard(): - self.test_imperative_api() - def test_big_dimension(self): paddle.disable_static() x = paddle.rand(shape=[2, 2, 2, 2, 2, 2, 2]) diff --git a/python/paddle/fluid/tests/unittests/test_maxout_op.py b/python/paddle/fluid/tests/unittests/test_maxout_op.py index 1554b246e8a6846bc4c3799cc3e975a6d6cd0c6a..7756f7d4ae841c38202aee1f80fb29e93af82ef8 100644 --- a/python/paddle/fluid/tests/unittests/test_maxout_op.py +++ b/python/paddle/fluid/tests/unittests/test_maxout_op.py @@ -20,7 +20,6 @@ from op_test import OpTest import paddle import paddle.fluid.core as core import paddle.nn.functional as F -from paddle.fluid.framework import _test_eager_guard paddle.enable_static() np.random.seed(1) @@ -108,7 +107,7 @@ class TestMaxoutAPI(unittest.TestCase): for r in res: np.testing.assert_allclose(out_ref, r, rtol=1e-05) - def func_test_dygraph_api(self): + def test_dygraph_api(self): paddle.disable_static(self.place) x = paddle.to_tensor(self.x_np) out1 = F.maxout(x, self.groups, self.axis) @@ -136,11 +135,6 @@ class TestMaxoutAPI(unittest.TestCase): x_float32 = paddle.fluid.data(name='x_float32', shape=[2, 4, 6, 8]) self.assertRaises(ValueError, F.maxout, x_float32, 2, 2) - def test_dygraph_api(self): - with _test_eager_guard(): - self.func_test_dygraph_api() - self.func_test_dygraph_api() - if __name__ == '__main__': unittest.main()