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未验证 提交 536a55fa 编写于 作者: W Weilong Wu 提交者： GitHub
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Support test_layers 51/55 tests with _test_eager_guard() (#39515)

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python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 536a55fa
......@@ -34,6 +34,7 @@ from test_imperative_base import new_program_scope
from paddle.fluid.dygraph import nn
from paddle.fluid.dygraph import base
from paddle.fluid.dygraph import to_variable
from paddle.fluid.framework import _test_eager_guard
class LayerTest(unittest.TestCase):
......@@ -98,6 +99,14 @@ 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)
self.assertTrue(np.array_equal(ret.numpy().shape, [3, 2]))
ret = custom(x, do_linear2=True)
self.assertTrue(np.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)
......@@ -121,6 +130,15 @@ class TestLayer(LayerTest):
static_ret, static_ret2 = self.get_static_graph_result(
feed={'data': inp}, fetch_list=[ret, ret2])
with self.dynamic_graph():
with _test_eager_guard():
t = base.to_variable(inp)
dropout = nn.Dropout(p=0.35, seed=1, is_test=False)
dy_eager_ret = dropout(t)
dy_eager_ret2 = fluid.layers.dropout(
t, dropout_prob=0.35, seed=1, is_test=False)
dy_eager_ret_value = dy_eager_ret.numpy()
dy_eager_ret2_value = dy_eager_ret2.numpy()
t = base.to_variable(inp)
dropout = nn.Dropout(p=0.35, seed=1, is_test=False)
dy_ret = dropout(t)
......@@ -129,6 +147,9 @@ class TestLayer(LayerTest):
dy_ret_value = dy_ret.numpy()
dy_ret2_value = dy_ret2.numpy()
self.assertTrue(np.array_equal(dy_eager_ret_value, dy_eager_ret2_value))
self.assertTrue(np.array_equal(static_ret, dy_eager_ret_value))
self.assertTrue(np.array_equal(static_ret, static_ret2))
self.assertTrue(np.array_equal(dy_ret_value, dy_ret2_value))
self.assertTrue(np.array_equal(static_ret, dy_ret_value))
......@@ -147,12 +168,22 @@ class TestLayer(LayerTest):
static_ret = self.get_static_graph_result(
feed={'data': inp}, fetch_list=[ret])[0]
with self.dynamic_graph():
with _test_eager_guard():
t = base.to_variable(inp)
linear = 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 = nn.Linear(
32, 4, bias_attr=fluid.initializer.ConstantInitializer(value=1))
dy_ret = linear(t)
dy_ret_value = dy_ret.numpy()
self.assertTrue(np.array_equal(static_ret, dy_eager_ret_value))
self.assertTrue(np.array_equal(static_ret, dy_ret_value))
with self.static_graph():
......@@ -193,11 +224,18 @@ class TestLayer(LayerTest):
static_ret = self.get_static_graph_result(
feed={'data': inp}, fetch_list=[ret])[0]
with self.dynamic_graph():
with _test_eager_guard():
t = base.to_variable(inp)
flatten = nn.Flatten()
dy_eager_ret = flatten(t)
dy_eager_ret_value = dy_eager_ret.numpy()
t = base.to_variable(inp)
flatten = nn.Flatten()
dy_ret = flatten(t)
dy_ret_value = dy_ret.numpy()
self.assertTrue(np.array_equal(static_ret, dy_eager_ret_value))
self.assertTrue(np.array_equal(static_ret, dy_ret_value))
with self.static_graph():
......@@ -253,13 +291,35 @@ class TestLayer(LayerTest):
static_ret2 = self.get_static_graph_result(
feed={'data': inp}, fetch_list=[ret])[0]
with self.dynamic_graph():
with _test_eager_guard():
lm = nn.LayerNorm(
normalized_shape=[32, 32],
bias_attr=fluid.initializer.ConstantInitializer(value=1),
act='sigmoid')
dy_eager_ret = lm(base.to_variable(inp))
dy_eager_ret_value = dy_eager_ret.numpy()
lm = nn.LayerNorm(
normalized_shape=[32, 32],
bias_attr=fluid.initializer.ConstantInitializer(value=1),
act='sigmoid')
dy_ret = lm(base.to_variable(inp))
dy_ret_value = dy_ret.numpy()
with self.dynamic_graph():
with _test_eager_guard():
lm = nn.LayerNorm(
normalized_shape=[32, 32],
shift=False,
scale=False,
param_attr=fluid.initializer.ConstantInitializer(value=1),
bias_attr=fluid.initializer.ConstantInitializer(value=1),
act='sigmoid')
lm(base.to_variable(inp))
self.assertFalse(hasattr(lm, "_scale_w"))
self.assertFalse(hasattr(lm, "_bias_w"))
lm = nn.LayerNorm(
normalized_shape=[32, 32],
shift=False,
......@@ -273,9 +333,18 @@ class TestLayer(LayerTest):
self.assertFalse(hasattr(lm, "_bias_w"))
self.assertTrue(np.array_equal(static_ret, static_ret2))
self.assertTrue(np.array_equal(dy_eager_ret_value, static_ret2))
self.assertTrue(np.array_equal(dy_ret_value, static_ret2))
with self.dynamic_graph():
with _test_eager_guard():
lm = nn.LayerNorm(
normalized_shape=[16, 32],
bias_attr=fluid.initializer.ConstantInitializer(value=1),
act='sigmoid')
with self.assertRaises(ValueError):
lm(base.to_variable(inp))
lm = nn.LayerNorm(
normalized_shape=[16, 32],
bias_attr=fluid.initializer.ConstantInitializer(value=1),
......@@ -295,11 +364,18 @@ class TestLayer(LayerTest):
fetch_list=[ret])[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()
self.assertTrue(np.array_equal(static_ret, dy_ret_value))
self.assertTrue(np.array_equal(static_ret, dy_eager_ret_value))
def test_relu(self):
with self.static_graph():
......@@ -310,11 +386,17 @@ class TestLayer(LayerTest):
[3, 3], dtype='float32')}, fetch_list=[ret])[0]
with self.dynamic_graph():
with _test_eager_guard():
t = np.ones([3, 3], dtype='float32')
dy_eager_ret = layers.relu(base.to_variable(t))
dy_eager_ret_value = dy_eager_ret.numpy()
t = np.ones([3, 3], dtype='float32')
dy_ret = layers.relu(base.to_variable(t))
dy_ret_value = dy_ret.numpy()
self.assertTrue(np.allclose(static_ret, dy_ret_value))
self.assertTrue(np.allclose(static_ret, dy_eager_ret_value))
def test_matmul(self):
with self.static_graph():
......@@ -331,12 +413,20 @@ class TestLayer(LayerTest):
fetch_list=[ret])[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 = layers.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 = layers.matmul(base.to_variable(t), base.to_variable(t2))
dy_ret_value = dy_ret.numpy()
self.assertTrue(np.allclose(static_ret, dy_ret_value))
self.assertTrue(np.allclose(static_ret, dy_eager_ret_value))
def test_conv2d(self):
with self.static_graph():
......@@ -358,6 +448,13 @@ class TestLayer(LayerTest):
fetch_list=[ret])[0]
with self.dynamic_graph():
with _test_eager_guard():
images = np.ones([2, 3, 5, 5], dtype='float32')
conv2d = nn.Conv2D(
num_channels=3, num_filters=3, filter_size=[2, 2])
dy_eager_ret = conv2d(base.to_variable(images))
dy_eager_ret_value = dy_eager_ret.numpy()
images = np.ones([2, 3, 5, 5], dtype='float32')
conv2d = nn.Conv2D(
num_channels=3, num_filters=3, filter_size=[2, 2])
......@@ -365,6 +462,16 @@ class TestLayer(LayerTest):
dy_ret_value = dy_ret.numpy()
with self.dynamic_graph():
with _test_eager_guard():
images = np.ones([2, 3, 5, 5], dtype='float32')
conv2d = nn.Conv2D(
num_channels=3,
num_filters=3,
filter_size=[2, 2],
bias_attr=False)
dy_ret = conv2d(base.to_variable(images))
self.assertTrue(conv2d.bias is None)
images = np.ones([2, 3, 5, 5], dtype='float32')
conv2d = nn.Conv2D(
num_channels=3,
......@@ -396,9 +503,49 @@ class TestLayer(LayerTest):
self.assertRaises(TypeError, test_type)
self.assertTrue(np.allclose(static_ret, dy_ret_value))
self.assertTrue(np.allclose(static_ret, dy_eager_ret_value))
self.assertTrue(np.allclose(static_ret, static_ret2))
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 = nn.Conv2D(
num_channels=3, num_filters=3, filter_size=[2, 2])
conv2d2 = nn.Conv2D(
num_channels=3,
num_filters=3,
filter_size=[2, 2],
param_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)
self.assertTrue(
np.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))
self.assertTrue(
np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))
conv2d2.weight = conv2d1.weight
conv2d2.bias = conv2d1.bias
self.assertTrue(
np.array_equal(conv2d1.weight.numpy(),
conv2d2.weight.numpy()))
self.assertTrue(
np.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(
......@@ -467,6 +614,14 @@ class TestLayer(LayerTest):
fetch_list=[updated_hidden, reset_hidden_pre, gate])
with self.dynamic_graph():
with _test_eager_guard():
gru = nn.GRUUnit(size=D * 3)
dy_eager_ret = gru(
base.to_variable(input), base.to_variable(hidden_input))
dy_eager_ret_value = []
for i in range(len(static_ret)):
dy_eager_ret_value.append(dy_eager_ret[i].numpy())
gru = nn.GRUUnit(size=D * 3)
dy_ret = gru(
base.to_variable(input), base.to_variable(hidden_input))
......@@ -477,8 +632,40 @@ class TestLayer(LayerTest):
for i in range(len(static_ret)):
self.assertTrue(np.allclose(static_ret[i], static_ret2[i]))
self.assertTrue(np.allclose(static_ret[i], dy_ret_value[i]))
self.assertTrue(np.allclose(static_ret[i], dy_eager_ret_value[i]))
with self.dynamic_graph():
with _test_eager_guard():
custom_weight = np.random.randn(D, D * 3).astype("float32")
weight_attr = fluid.ParamAttr(
initializer=fluid.initializer.NumpyArrayInitializer(
custom_weight))
gru1 = nn.GRUUnit(size=D * 3)
gru2 = nn.GRUUnit(size=D * 3, param_attr=weight_attr)
dy_ret1 = gru1(
base.to_variable(input), base.to_variable(hidden_input))
dy_ret2 = gru2(
base.to_variable(input), base.to_variable(hidden_input))
self.assertFalse(
np.array_equal(gru1.weight.numpy(), gru2.weight.numpy()))
for o1, o2 in zip(dy_ret1, dy_ret2):
self.assertFalse(np.array_equal(o1.numpy(), o2.numpy()))
gru2.weight.set_value(gru1.weight.numpy())
gru2.bias.set_value(gru1.bias)
dy_ret1 = gru1(
base.to_variable(input), base.to_variable(hidden_input))
dy_ret2 = gru2(
base.to_variable(input), base.to_variable(hidden_input))
for o1, o2 in zip(dy_ret1, dy_ret2):
self.assertTrue(np.array_equal(o1.numpy(), o2.numpy()))
gru2.weight = gru1.weight
gru2.bias = gru1.bias
self.assertTrue(
np.array_equal(gru1.weight.numpy(), gru2.weight.numpy()))
self.assertTrue(
np.array_equal(gru1.bias.numpy(), gru2.bias.numpy()))
custom_weight = np.random.randn(D, D * 3).astype("float32")
weight_attr = fluid.ParamAttr(
initializer=fluid.initializer.NumpyArrayInitializer(
......@@ -543,19 +730,37 @@ class TestLayer(LayerTest):
fetch_list=[ret])[0]
with self.dynamic_graph():
with _test_eager_guard():
ret = layers.elementwise_add(to_variable(n), to_variable(n2))
ret = layers.elementwise_pow(ret, to_variable(n3))
ret = layers.elementwise_div(ret, to_variable(n4))
ret = layers.elementwise_sub(ret, to_variable(n5))
dy_eager_ret = layers.elementwise_mul(ret, to_variable(n6))
dy_eager_ret_value = dy_eager_ret.numpy()
ret = layers.elementwise_add(to_variable(n), to_variable(n2))
ret = layers.elementwise_pow(ret, to_variable(n3))
ret = layers.elementwise_div(ret, to_variable(n4))
ret = layers.elementwise_sub(ret, to_variable(n5))
dy_ret = layers.elementwise_mul(ret, to_variable(n6))
dy_ret_value = dy_ret.numpy()
self.assertTrue(np.allclose(static_ret, dy_ret_value))
self.assertTrue(np.allclose(static_ret, dy_eager_ret_value))
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 = layers.elementwise_min(
to_variable(n), to_variable(n2))
max_eager_ret = layers.elementwise_max(
to_variable(n), to_variable(n2))
min_eager_ret_value = min_eager_ret.numpy()
max_eager_ret_value = max_eager_ret.numpy()
min_ret = layers.elementwise_min(to_variable(n), to_variable(n2))
max_ret = layers.elementwise_max(to_variable(n), to_variable(n2))
min_ret_value = min_ret.numpy()
......@@ -563,6 +768,8 @@ class TestLayer(LayerTest):
self.assertTrue(np.allclose(n, min_ret_value))
self.assertTrue(np.allclose(n2, max_ret_value))
self.assertTrue(np.allclose(n, min_eager_ret_value))
self.assertTrue(np.allclose(n2, max_eager_ret_value))
def test_sequence_conv(self):
inp_np = np.arange(12).reshape([3, 4]).astype('float32')
......@@ -633,6 +840,16 @@ class TestLayer(LayerTest):
static_rlt2 = self.get_static_graph_result(
feed={'pixel': inp_np}, fetch_list=[out])[0]
with self.dynamic_graph():
with _test_eager_guard():
conv2d_transpose = nn.Conv2DTranspose(
num_channels=3,
num_filters=10,
filter_size=27,
act='sigmoid',
bias_attr=fluid.initializer.ConstantInitializer(value=1))
dy_eager_rlt = conv2d_transpose(base.to_variable(inp_np))
dy_eager_rlt_value = dy_eager_rlt.numpy()
conv2d_transpose = nn.Conv2DTranspose(
num_channels=3,
num_filters=10,
......@@ -643,8 +860,48 @@ class TestLayer(LayerTest):
dy_rlt_value = dy_rlt.numpy()
self.assertTrue(np.allclose(static_rlt2, static_rlt))
self.assertTrue(np.allclose(dy_rlt_value, static_rlt2))
self.assertTrue(np.allclose(dy_eager_rlt_value, static_rlt2))
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 = nn.Conv2DTranspose(
num_channels=3, num_filters=3, filter_size=[2, 2])
conv2d2 = nn.Conv2DTranspose(
num_channels=3,
num_filters=3,
filter_size=[2, 2],
param_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)
self.assertTrue(
np.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))
self.assertTrue(
np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))
conv2d2.weight = conv2d1.weight
conv2d2.bias = conv2d1.bias
self.assertTrue(
np.array_equal(conv2d1.weight.numpy(),
conv2d2.weight.numpy()))
self.assertTrue(
np.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(
......@@ -750,6 +1007,17 @@ 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 = nn.BilinearTensorProduct(
3,
3,
6,
bias_attr=fluid.initializer.ConstantInitializer(value=1),
act='sigmoid')
dy_eager_rlt = btp(
base.to_variable(inp_np_x), base.to_variable(inp_np_y))
dy_eager_rlt_value = dy_eager_rlt.numpy()
btp = nn.BilinearTensorProduct(
3,
3,
......@@ -758,11 +1026,19 @@ class TestLayer(LayerTest):
act='sigmoid')
dy_rlt = btp(base.to_variable(inp_np_x), base.to_variable(inp_np_y))
dy_rlt_value = dy_rlt.numpy()
with self.dynamic_graph():
with _test_eager_guard():
btp2 = nn.BilinearTensorProduct(3, 3, 6, act='sigmoid')
dy_eager_rlt2 = btp2(
base.to_variable(inp_np_x), base.to_variable(inp_np_y))
dy_eager_rlt2_value = dy_eager_rlt2.numpy()
btp2 = nn.BilinearTensorProduct(3, 3, 6, act='sigmoid')
dy_rlt2 = btp2(
base.to_variable(inp_np_x), base.to_variable(inp_np_y))
dy_rlt2_value = dy_rlt2.numpy()
with self.static_graph():
data_x2 = layers.data(
name='x',
......@@ -782,10 +1058,42 @@ class TestLayer(LayerTest):
'y': inp_np_y}, fetch_list=[out2])[0]
self.assertTrue(np.array_equal(dy_rlt2_value, static_rlt3))
self.assertTrue(np.array_equal(dy_eager_rlt2_value, static_rlt3))
self.assertTrue(np.array_equal(static_rlt2, static_rlt))
self.assertTrue(np.array_equal(dy_rlt_value, static_rlt))
self.assertTrue(np.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 = nn.BilinearTensorProduct(3, 3, 6, act='sigmoid')
btp2 = nn.BilinearTensorProduct(
3, 3, 6, act='sigmoid', param_attr=weight_attr)
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))
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))
self.assertTrue(
np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy()))
btp2.weight = btp1.weight
btp2.bias = btp1.bias
self.assertTrue(
np.array_equal(btp1.weight.numpy(), btp2.weight.numpy()))
self.assertTrue(
np.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(
......@@ -842,6 +1150,15 @@ class TestLayer(LayerTest):
feed={"input": inp_np}, fetch_list=[out])[0]
with self.dynamic_graph():
with _test_eager_guard():
prelu = nn.PRelu(
mode=mode,
channel=inp_np.shape[1],
input_shape=inp_np.shape,
param_attr=ParamAttr(initializer=Constant(1.0)))
dy_eager_rlt = prelu(base.to_variable(inp_np))
dy_eager_rlt_value = dy_eager_rlt.numpy()
prelu = nn.PRelu(
mode=mode,
channel=inp_np.shape[1],
......@@ -852,8 +1169,40 @@ 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():
inp_np = np.random.randn(5, 200, 100, 100).astype("float32")
inp = base.to_variable(inp_np)
prelu1 = nn.PRelu(
mode=mode,
channel=inp_np.shape[1],
input_shape=inp_np.shape,
param_attr=ParamAttr(initializer=Constant(2.0)))
prelu2 = nn.PRelu(
mode=mode,
channel=inp_np.shape[1],
input_shape=inp_np.shape,
param_attr=ParamAttr(initializer=Constant(1.0)))
dy_rlt1 = prelu1(inp)
dy_rlt2 = prelu2(inp)
self.assertFalse(
np.array_equal(prelu1.weight.numpy(), prelu2.weight.numpy(
)))
self.assertFalse(
np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy()))
prelu2.weight.set_value(prelu1.weight.numpy())
dy_rlt1 = prelu1(inp)
dy_rlt2 = prelu2(inp)
self.assertTrue(
np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy()))
prelu2.weight = prelu1.weight
self.assertTrue(
np.array_equal(prelu1.weight.numpy(), prelu2.weight.numpy(
)))
inp_np = np.random.randn(5, 200, 100, 100).astype("float32")
inp = base.to_variable(inp_np)
prelu1 = nn.PRelu(
......@@ -905,6 +1254,14 @@ class TestLayer(LayerTest):
static_rlt2 = self.get_static_graph_result(
feed={'word': inp_word}, fetch_list=[emb_rlt])[0]
with self.dynamic_graph():
with _test_eager_guard():
emb2 = nn.Embedding(
size=[dict_size, 32],
param_attr='eager_emb.w',
is_sparse=False)
dy_eager_rlt = emb2(base.to_variable(inp_word))
dy_eager_rlt_value = dy_eager_rlt.numpy()
emb2 = nn.Embedding(
size=[dict_size, 32], param_attr='emb.w', is_sparse=False)
dy_rlt = emb2(base.to_variable(inp_word))
......@@ -912,8 +1269,34 @@ 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 = nn.Embedding(size=[dict_size, 32], is_sparse=False)
emb2 = nn.Embedding(
size=[dict_size, 32],
param_attr=weight_attr,
is_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))
self.assertTrue(
np.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))
self.assertTrue(np.array_equal(rep1.numpy(), rep2.numpy()))
emb2.weight = emb1.weight
self.assertTrue(
np.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(
......@@ -1018,6 +1401,7 @@ class TestLayer(LayerTest):
feed=feed_dict, fetch_list=[nce_loss2])[0]
with self.dynamic_graph():
# TODO(wuweilong): Add with _test_eager_guard():
words = []
for i in range(window_size):
words.append(base.to_variable(inp_word[i]))
......@@ -1054,6 +1438,7 @@ class TestLayer(LayerTest):
self.assertTrue(np.allclose(dy_rlt_value, static_rlt))
with self.dynamic_graph():
# TODO(wuweilong): Add with _test_eager_guard():
custom_weight = np.random.randn(dict_size, 128).astype("float32")
weight_attr = fluid.ParamAttr(
initializer=fluid.initializer.NumpyArrayInitializer(
......@@ -1118,6 +1503,17 @@ 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])))
self.assertTrue(
np.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(
......@@ -1127,6 +1523,16 @@ 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 = fluid.layers.split(input, num_or_sections=2, dim=1)
x00, x11 = fluid.layers.split(
input,
num_or_sections=2,
dim=fluid.dygraph.to_variable(np.array([1])))
self.assertTrue(np.array_equal(x0.numpy(), x00.numpy()))
self.assertTrue(np.array_equal(x1.numpy(), x11.numpy()))
input = fluid.dygraph.to_variable(np.random.random((3, 8, 5)))
x0, x1 = fluid.layers.split(input, num_or_sections=2, dim=1)
x00, x11 = fluid.layers.split(
......@@ -1138,6 +1544,17 @@ 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 = layers.topk(input, k=5)
top5_values2, top5_indices2 = layers.topk(
input, k=fluid.dygraph.to_variable(np.array([5])))
self.assertTrue(
np.array_equal(top5_values1.numpy(), top5_values2.numpy()))
self.assertTrue(
np.array_equal(top5_indices1.numpy(), top5_indices2.numpy(
)))
input = fluid.dygraph.to_variable(np.random.random((13, 11)))
top5_values1, top5_indices1 = layers.topk(input, k=5)
top5_values2, top5_indices2 = layers.topk(
......@@ -1168,15 +1585,61 @@ class TestLayer(LayerTest):
fetch_list=[ret])[0]
with self.dynamic_graph():
with _test_eager_guard():
images = np.ones([2, 3, 6, 6, 6], dtype='float32')
conv3d = nn.Conv3D(num_channels=3, num_filters=3, filter_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 = nn.Conv3D(num_channels=3, num_filters=3, filter_size=2)
dy_ret = conv3d(base.to_variable(images))
dy_rlt_value = dy_ret.numpy()
self.assertTrue(np.allclose(static_ret, dy_rlt_value))
self.assertTrue(np.allclose(static_ret, dy_eager_rlt_value))
self.assertTrue(np.allclose(static_ret, static_ret2))
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 = nn.Conv3D(
num_channels=3, num_filters=3, filter_size=2)
conv3d2 = nn.Conv3D(
num_channels=3,
num_filters=3,
filter_size=2,
param_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)
self.assertTrue(
np.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))
self.assertTrue(
np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))
conv3d2.weight = conv3d1.weight
conv3d2.bias = conv3d1.bias
self.assertTrue(
np.array_equal(conv3d1.weight.numpy(),
conv3d2.weight.numpy()))
self.assertTrue(
np.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(
......@@ -1309,6 +1772,7 @@ class TestLayer(LayerTest):
with_lod=True)[0]
with self.dynamic_graph():
# TODO(wuweilong): Add with _test_eager_guard():
groupNorm = nn.GroupNorm(
channels=shape[1],
groups=2,
......@@ -1347,17 +1811,29 @@ class TestLayer(LayerTest):
feed={'X': input}, fetch_list=[ret])[0]
with self.dynamic_graph():
with _test_eager_guard():
instanceNorm = nn.InstanceNorm(num_channels=shape[1])
dy_eager_ret = instanceNorm(base.to_variable(input))
dy_eager_rlt_value = dy_eager_ret.numpy()
instanceNorm = nn.InstanceNorm(num_channels=shape[1])
dy_ret = instanceNorm(base.to_variable(input))
dy_rlt_value = dy_ret.numpy()
with self.dynamic_graph():
with _test_eager_guard():
instanceNorm = nn.InstanceNorm(num_channels=shape[1])
dy_eager_ret = instanceNorm(base.to_variable(input))
dy_eager_rlt_value2 = dy_eager_ret.numpy()
instanceNorm = nn.InstanceNorm(num_channels=shape[1])
dy_ret = instanceNorm(base.to_variable(input))
dy_rlt_value2 = dy_ret.numpy()
self.assertTrue(np.allclose(static_ret, dy_rlt_value))
self.assertTrue(np.allclose(static_ret, dy_rlt_value2))
self.assertTrue(np.allclose(static_ret, dy_eager_rlt_value))
self.assertTrue(np.allclose(static_ret, dy_eager_rlt_value2))
self.assertTrue(np.allclose(static_ret, static_ret2))
with self.static_graph():
......@@ -1420,11 +1896,17 @@ class TestLayer(LayerTest):
with_lod=True)[0]
with self.dynamic_graph():
with _test_eager_guard():
spectralNorm = nn.SpectralNorm(shape, dim=1, power_iters=2)
dy_eager_ret = spectralNorm(base.to_variable(input))
dy_eager_rlt_value = dy_eager_ret.numpy()
spectralNorm = nn.SpectralNorm(shape, dim=1, power_iters=2)
dy_ret = spectralNorm(base.to_variable(input))
dy_rlt_value = dy_ret.numpy()
self.assertTrue(np.allclose(static_ret, dy_rlt_value))
self.assertTrue(np.allclose(static_ret, dy_eager_rlt_value))
self.assertTrue(np.allclose(static_ret, static_ret2))
def test_tree_conv(self):
......@@ -1492,6 +1974,13 @@ class TestLayer(LayerTest):
with_lod=False)[0]
with self.dynamic_graph():
with _test_eager_guard():
treeConv = nn.TreeConv(
feature_size=5, output_size=6, num_filters=1, max_depth=2)
dy_eager_ret = treeConv(
base.to_variable(vectors), base.to_variable(adj))
dy_eager_rlt_value = dy_eager_ret.numpy()
treeConv = nn.TreeConv(
feature_size=5, output_size=6, num_filters=1, max_depth=2)
dy_ret = treeConv(base.to_variable(vectors), base.to_variable(adj))
......@@ -1499,8 +1988,51 @@ class TestLayer(LayerTest):
self.assertTrue(np.allclose(static_ret, static_ret2))
self.assertTrue(np.allclose(static_ret, dy_rlt_value))
self.assertTrue(np.allclose(static_ret, dy_eager_rlt_value))
with self.dynamic_graph():
with _test_eager_guard():
custom_weight = np.random.randn(5, 3, 6, 1).astype("float32")
weight_attr = fluid.ParamAttr(
initializer=fluid.initializer.NumpyArrayInitializer(
custom_weight))
treeConv1 = nn.TreeConv(
feature_size=5,
output_size=6,
num_filters=1,
max_depth=2,
bias_attr='eager_tc1_b')
treeConv2 = nn.TreeConv(
feature_size=5,
output_size=6,
num_filters=1,
max_depth=2,
param_attr=weight_attr,
bias_attr='eager_tc2_b')
dy_ret1 = treeConv1(
base.to_variable(vectors), base.to_variable(adj))
dy_ret2 = treeConv2(
base.to_variable(vectors), base.to_variable(adj))
self.assertFalse(
np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))
treeConv2.weight.set_value(treeConv1.weight.numpy())
treeConv2.bias.set_value(treeConv1.bias)
dy_ret1 = treeConv1(
base.to_variable(vectors), base.to_variable(adj))
dy_ret2 = treeConv2(
base.to_variable(vectors), base.to_variable(adj))
self.assertTrue(
np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))
treeConv2.weight = treeConv1.weight
treeConv2.bias = treeConv1.bias
self.assertTrue(
np.array_equal(treeConv1.weight.numpy(),
treeConv2.weight.numpy()))
self.assertTrue(
np.array_equal(treeConv1.bias.numpy(),
treeConv2.bias.numpy()))
custom_weight = np.random.randn(5, 3, 6, 1).astype("float32")
weight_attr = fluid.ParamAttr(
initializer=fluid.initializer.NumpyArrayInitializer(
......@@ -1557,14 +2089,69 @@ class TestLayer(LayerTest):
static_rlt2 = self.get_static_graph_result(
feed={'pixel': input_array}, fetch_list=[out])[0]
with self.dynamic_graph():
with _test_eager_guard():
conv3d_transpose = nn.Conv3DTranspose(
num_channels=3,
num_filters=12,
filter_size=12,
use_cudnn=False)
dy_eager_rlt = conv3d_transpose(base.to_variable(input_array))
dy_eager_rlt_value = dy_eager_rlt.numpy()
conv3d_transpose = nn.Conv3DTranspose(
num_channels=3, num_filters=12, filter_size=12, use_cudnn=False)
dy_rlt = conv3d_transpose(base.to_variable(input_array))
dy_rlt_value = dy_rlt.numpy()
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():
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 = nn.Conv3DTranspose(
num_channels=3,
num_filters=3,
filter_size=2,
bias_attr='eager_conv3d1_b',
use_cudnn=False)
conv3d2 = nn.Conv3DTranspose(
num_channels=3,
num_filters=3,
filter_size=2,
param_attr=weight_attr,
bias_attr='eager_conv3d2_b',
use_cudnn=False)
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)
self.assertTrue(
np.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))
self.assertTrue(
np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))
conv3d2.weight = conv3d1.weight
conv3d2.bias = conv3d1.bias
self.assertTrue(
np.array_equal(conv3d1.weight.numpy(),
conv3d2.weight.numpy()))
self.assertTrue(
np.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(
......@@ -1614,6 +2201,20 @@ class TestLayer(LayerTest):
stack_rlt2 = np.stack(array_rlt2, axis=0)
with self.dynamic_graph():
with _test_eager_guard():
eager_eye_tensor = layers.eye(num_rows=3, num_columns=2)
eager_eye_tensor_rlt1 = layers.eye(num_rows=3,
num_columns=2,
batch_shape=[3])
eager_eye_tensor_rlt2 = layers.eye(num_rows=3,
num_columns=2,
batch_shape=[4, 3])
eager_diag_tensor = layers.eye(20)
eager_eye_tensor_value = eager_eye_tensor.numpy()
eager_eye_tensor_rlt1_value = eager_eye_tensor_rlt1.numpy()
eager_eye_tensor_rlt2_value = eager_eye_tensor_rlt2.numpy()
eager_diag_tensor_value = eager_diag_tensor.numpy()
eye_tensor = layers.eye(num_rows=3, num_columns=2)
eye_tensor_rlt1 = layers.eye(num_rows=3,
num_columns=2,
......@@ -1626,6 +2227,12 @@ class TestLayer(LayerTest):
eye_tensor_rlt1_value = eye_tensor_rlt1.numpy()
eye_tensor_rlt2_value = eye_tensor_rlt2.numpy()
diag_tensor_value = diag_tensor.numpy()
self.assertTrue(np.allclose(eager_eye_tensor_value, np_eye))
self.assertTrue(np.allclose(eager_eye_tensor_rlt1_value, stack_rlt1))
self.assertTrue(np.allclose(eager_eye_tensor_rlt2_value, stack_rlt2))
self.assertTrue(np.allclose(eager_diag_tensor_value, np.eye(20)))
self.assertTrue(np.allclose(eye_tensor_value, np_eye))
self.assertTrue(np.allclose(eye_tensor_rlt1_value, stack_rlt1))
self.assertTrue(np.allclose(eye_tensor_rlt2_value, stack_rlt2))
......@@ -1655,6 +2262,7 @@ class TestLayer(LayerTest):
static_ret = self.get_static_graph_result(feed={}, fetch_list=out)
with self.dynamic_graph():
# TODO(wuweilong): Add with _test_eager_guard():
i = layers.fill_constant(shape=[1], dtype='int64', value=0)
ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
......@@ -1687,6 +2295,14 @@ 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 = layers.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 = layers.less_than(x=da, y=db)
......@@ -1703,6 +2319,14 @@ 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 = layers.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 = layers.less_equal(x=da1, y=db1)
......@@ -1719,6 +2343,14 @@ 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 = layers.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 = layers.greater_than(x=da2, y=db2)
......@@ -1735,6 +2367,14 @@ 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 = layers.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 = layers.greater_equal(x=da3, y=db3)
......@@ -1751,6 +2391,14 @@ 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 = layers.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 = layers.equal(x=da4, y=db4)
......@@ -1767,6 +2415,14 @@ 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 = layers.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 = layers.equal(x=da5, y=db5)
......@@ -1795,6 +2451,23 @@ 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 = layers.cond(a < b, lambda: less_than_branch(a, b),
lambda: greater_equal_branch(a, b))
out2 = layers.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()
self.assertTrue(
np.array_equal(eager_dynamic_res, eager_dynamic_res2))
with self.assertRaises(TypeError):
layers.cond(a < b, 'str', 'str')
with self.assertRaises(TypeError):
layers.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 = layers.cond(a < b, lambda: less_than_branch(a, b),
......@@ -1810,6 +2483,7 @@ class TestLayer(LayerTest):
layers.cond(a >= b, 'str', 'str')
self.assertTrue(np.array_equal(static_res, dynamic_res))
self.assertTrue(np.array_equal(static_res, eager_dynamic_res))
def test_case(self):
def fn_1():
......@@ -1840,6 +2514,23 @@ 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 = layers.less_than(z, x) # true: 0.2 < 0.3
pred_2 = layers.less_than(x, y) # false: 0.3 < 0.1
pred_3 = layers.equal(x, y) # false: 0.3 == 0.1
out_1 = layers.case(
pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)],
default=fn_3)
out_2 = layers.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)
......@@ -1856,6 +2547,8 @@ class TestLayer(LayerTest):
self.assertTrue(np.array_equal(static_res1, dynamic_res1))
self.assertTrue(np.array_equal(static_res2, dynamic_res2))
self.assertTrue(np.array_equal(static_res1, eager_dynamic_res1))
self.assertTrue(np.array_equal(static_res2, eager_dynamic_res2))
def test_switch_case(self):
def fn_1():
......@@ -1891,6 +2584,29 @@ class TestLayer(LayerTest):
fetch_list=[out_1, out_2, out_3])
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 = layers.switch_case(
branch_index=index_1,
branch_fns={1: fn_1,
2: fn_2},
default=fn_3)
out_2 = layers.switch_case(
branch_index=index_2,
branch_fns=[(1, fn_1), (2, fn_2)],
default=fn_3)
out_3 = layers.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)
......@@ -1914,6 +2630,9 @@ class TestLayer(LayerTest):
self.assertTrue(np.array_equal(static_res1, dynamic_res1))
self.assertTrue(np.array_equal(static_res2, dynamic_res2))
self.assertTrue(np.array_equal(static_res3, dynamic_res3))
self.assertTrue(np.array_equal(static_res1, eager_dynamic_res1))
self.assertTrue(np.array_equal(static_res2, eager_dynamic_res2))
self.assertTrue(np.array_equal(static_res3, eager_dynamic_res3))
def test_crop_tensor(self):
with self.static_graph():
......@@ -3281,6 +4000,14 @@ class TestBook(LayerTest):
fetch_list=[output])[0]
with self.dynamic_graph():
with _test_eager_guard():
x_dy = base.to_variable(x_np)
rois_dy = base.to_variable(rois_np)
rois_num_dy = base.to_variable(rois_num_np)
dy_eager_res = layers.roi_pool(
x_dy, rois_dy, 4, 4, 0.5, rois_num=rois_num_dy)
dy_eager_res_value = dy_eager_res[0].numpy()
x_dy = base.to_variable(x_np)
rois_dy = base.to_variable(rois_np)
rois_num_dy = base.to_variable(rois_num_np)
......@@ -3288,6 +4015,7 @@ class TestBook(LayerTest):
x_dy, rois_dy, 4, 4, 0.5, rois_num=rois_num_dy)
dy_res_value = dy_res[0].numpy()
self.assertTrue(np.array_equal(static_res, dy_res_value))
self.assertTrue(np.array_equal(static_res, dy_eager_res_value))
def test_sequence_enumerate(self):
# TODO(minqiyang): dygraph do not support lod now
......@@ -3312,12 +4040,21 @@ class TestBook(LayerTest):
fetch_list=[output])[0]
with self.dynamic_graph():
with _test_eager_guard():
x_dy = base.to_variable(x_np)
rois_dy = base.to_variable(rois_np)
rois_num_dy = base.to_variable(rois_num_np)
dy_eager_res = layers.roi_align(
x_dy, rois_dy, 4, 4, 0.5, 2, rois_num=rois_num_dy)
dy_eager_res_value = dy_eager_res.numpy()
x_dy = base.to_variable(x_np)
rois_dy = base.to_variable(rois_np)
rois_num_dy = base.to_variable(rois_num_np)
dy_res = layers.roi_align(
x_dy, rois_dy, 4, 4, 0.5, 2, rois_num=rois_num_dy)
dy_res_value = dy_res.numpy()
self.assertTrue(np.array_equal(static_res, dy_eager_res_value))
self.assertTrue(np.array_equal(static_res, dy_res_value))
def test_dice_loss(self):
......@@ -3338,11 +4075,18 @@ class TestBook(LayerTest):
fetch_list=[output])[0]
with self.dynamic_graph():
with _test_eager_guard():
input_ = base.to_variable(input_np)
label_ = base.to_variable(label_np)
dy_eager_res = layers.dice_loss(input_, label_, eps)
dy_eager_res_value = dy_eager_res.numpy()
input_ = base.to_variable(input_np)
label_ = base.to_variable(label_np)
dy_res = layers.dice_loss(input_, label_, eps)
dy_res_value = dy_res.numpy()
self.assertTrue(np.array_equal(static_res, dy_res_value))
self.assertTrue(np.array_equal(static_res, dy_eager_res_value))
def test_roi_perspective_transform(self):
# TODO(minqiyang): dygraph do not support lod now
......
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