未验证 提交 78ff5d72 编写于 作者: L lujun 提交者: GitHub

Merge pull request #16520 from PaddlePaddle/move-code

add some dygraph op
1.Conv3d
2.Conv3dTranspose
3.RowConv
4.GroupNorm
5.SpectralNorm
6.TreeConv

and utest
此差异已折叠。
......@@ -81,6 +81,7 @@ list(REMOVE_ITEM TEST_OPS test_imperative_resnet)
list(REMOVE_ITEM TEST_OPS test_imperative_se_resnext)
list(REMOVE_ITEM TEST_OPS test_imperative_mnist)
list(REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer)
list(REMOVE_ITEM TEST_OPS test_layers)
foreach(TEST_OP ${TEST_OPS})
py_test_modules(${TEST_OP} MODULES ${TEST_OP})
endforeach(TEST_OP)
......@@ -118,7 +119,7 @@ py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SE
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 450)
py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
py_test_modules(test_layers MODULES test_layers ENVS FLAGS_cudnn_deterministic=1)
if(NOT WIN32)
py_test_modules(test_ir_memory_optimize_transformer MODULES test_ir_memory_optimize_transformer SERIAL)
endif()
......
......@@ -595,6 +595,280 @@ class TestLayer(LayerTest):
self.assertTrue(np.allclose(static_rlt2, static_rlt))
self.assertTrue(np.allclose(nce_loss3._numpy(), static_rlt))
def test_conv3d(self):
with self.static_graph():
images = layers.data(
name='pixel', shape=[3, 6, 6, 6], dtype='float32')
ret = layers.conv3d(input=images, num_filters=3, filter_size=2)
static_ret = self.get_static_graph_result(
feed={'pixel': np.ones(
[2, 3, 6, 6, 6], dtype='float32')},
fetch_list=[ret])[0]
with self.static_graph():
images = layers.data(
name='pixel', shape=[3, 6, 6, 6], dtype='float32')
conv3d = nn.Conv3D('conv3d', num_filters=3, filter_size=2)
ret = conv3d(images)
static_ret2 = self.get_static_graph_result(
feed={'pixel': np.ones(
[2, 3, 6, 6, 6], dtype='float32')},
fetch_list=[ret])[0]
with self.dynamic_graph():
images = np.ones([2, 3, 6, 6, 6], dtype='float32')
conv3d = nn.Conv3D('conv3d', num_filters=3, filter_size=2)
dy_ret = conv3d(base.to_variable(images))
self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
self.assertTrue(np.allclose(static_ret, static_ret2))
def test_row_conv(self):
input = np.arange(15).reshape([3, 5]).astype('float32')
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
with self.static_graph():
x = layers.data(
name='X',
shape=[3, 5],
dtype='float32',
lod_level=1,
append_batch_size=False)
ret = layers.row_conv(input=x, future_context_size=2)
static_ret = self.get_static_graph_result(
feed={
'X': fluid.create_lod_tensor(
data=input, recursive_seq_lens=[[1, 1, 1]], place=place)
},
fetch_list=[ret],
with_lod=True)[0]
with self.static_graph():
x = layers.data(
name='X',
shape=[3, 5],
dtype='float32',
lod_level=1,
append_batch_size=False)
rowConv = nn.RowConv('RowConv', future_context_size=2)
ret = rowConv(x)
static_ret2 = self.get_static_graph_result(
feed={
'X': fluid.create_lod_tensor(
data=input, recursive_seq_lens=[[1, 1, 1]], place=place)
},
fetch_list=[ret],
with_lod=True)[0]
# TODO: dygraph can't support LODTensor
self.assertTrue(np.allclose(static_ret, static_ret2))
def test_group_norm(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
shape = (2, 4, 3, 3)
input = np.random.random(shape).astype('float32')
with self.static_graph():
X = fluid.layers.data(
name='X',
shape=shape,
dtype='float32',
lod_level=1,
append_batch_size=False)
ret = layers.group_norm(input=X, groups=2)
static_ret = self.get_static_graph_result(
feed={
'X': fluid.create_lod_tensor(
data=input, recursive_seq_lens=[[1, 1]], place=place)
},
fetch_list=[ret],
with_lod=True)[0]
with self.static_graph():
X = fluid.layers.data(
name='X',
shape=shape,
dtype='float32',
lod_level=1,
append_batch_size=False)
groupNorm = nn.GroupNorm('GroupNorm', groups=2)
ret = groupNorm(X)
static_ret2 = self.get_static_graph_result(
feed={
'X': fluid.create_lod_tensor(
data=input, recursive_seq_lens=[[1, 1]], place=place)
},
fetch_list=[ret],
with_lod=True)[0]
with self.dynamic_graph():
groupNorm = nn.GroupNorm('GroupNorm', groups=2)
dy_ret = groupNorm(base.to_variable(input))
self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
self.assertTrue(np.allclose(static_ret, static_ret2))
def test_spectral_norm(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
shape = (2, 4, 3, 3)
input = np.random.random(shape).astype('float32')
with self.static_graph():
Weight = fluid.layers.data(
name='Weight',
shape=shape,
dtype='float32',
lod_level=1,
append_batch_size=False)
ret = layers.spectral_norm(weight=Weight, dim=1, power_iters=2)
static_ret = self.get_static_graph_result(
feed={
'Weight': fluid.create_lod_tensor(
data=input, recursive_seq_lens=[[1, 1]], place=place),
},
fetch_list=[ret],
with_lod=True)[0]
with self.static_graph():
Weight = fluid.layers.data(
name='Weight',
shape=shape,
dtype='float32',
lod_level=1,
append_batch_size=False)
spectralNorm = nn.SpectralNorm('SpectralNorm', dim=1, power_iters=2)
ret = spectralNorm(Weight)
static_ret2 = self.get_static_graph_result(
feed={
'Weight': fluid.create_lod_tensor(
data=input, recursive_seq_lens=[[1, 1]], place=place)
},
fetch_list=[ret],
with_lod=True)[0]
with self.dynamic_graph():
spectralNorm = nn.SpectralNorm('SpectralNorm', dim=1, power_iters=2)
dy_ret = spectralNorm(base.to_variable(input))
self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
self.assertTrue(np.allclose(static_ret, static_ret2))
def test_tree_conv(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
adj_array = [1, 2, 1, 3, 1, 4, 1, 5, 2, 6, 2, 7, 2, 8, 4, 9, 4, 10]
adj = np.array(adj_array).reshape((1, 9, 2)).astype('int32')
adj = np.tile(adj, (1, 1, 1))
vectors = np.random.random((1, 10, 5)).astype('float32')
with self.static_graph():
NodesVector = fluid.layers.data(
name='NodesVector',
shape=(1, 10, 5),
dtype='float32',
lod_level=1,
append_batch_size=False)
EdgeSet = fluid.layers.data(
name='EdgeSet',
shape=(1, 9, 2),
dtype='int32',
lod_level=1,
append_batch_size=False)
ret = layers.tree_conv(
nodes_vector=NodesVector,
edge_set=EdgeSet,
output_size=6,
num_filters=1,
max_depth=2)
static_ret = self.get_static_graph_result(
feed={
'NodesVector': fluid.create_lod_tensor(
data=vectors, recursive_seq_lens=[[1]], place=place),
'EdgeSet': fluid.create_lod_tensor(
data=adj, recursive_seq_lens=[[1]], place=place)
},
fetch_list=[ret],
with_lod=False)[0]
with self.static_graph():
NodesVector = fluid.layers.data(
name='NodesVector',
shape=(1, 10, 5),
dtype='float32',
lod_level=1,
append_batch_size=False)
EdgeSet = fluid.layers.data(
name='EdgeSet',
shape=(1, 9, 2),
dtype='int32',
lod_level=1,
append_batch_size=False)
treeConv = nn.TreeConv(
'TreeConv', output_size=6, num_filters=1, max_depth=2)
ret = treeConv(NodesVector, EdgeSet)
static_ret2 = self.get_static_graph_result(
feed={
'NodesVector': fluid.create_lod_tensor(
data=vectors, recursive_seq_lens=[[1]], place=place),
'EdgeSet': fluid.create_lod_tensor(
data=adj, recursive_seq_lens=[[1]], place=place)
},
fetch_list=[ret],
with_lod=False)[0]
with self.dynamic_graph():
treeConv = nn.TreeConv(
'SpectralNorm', output_size=6, num_filters=1, max_depth=2)
dy_ret = treeConv(base.to_variable(vectors), base.to_variable(adj))
self.assertTrue(np.allclose(static_ret, static_ret2))
self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
def test_conv3d_transpose(self):
input_array = np.arange(0, 48).reshape(
[2, 3, 2, 2, 2]).astype('float32')
with self.static_graph():
img = layers.data(name='pixel', shape=[3, 2, 2, 2], dtype='float32')
out = layers.conv3d_transpose(
input=img, num_filters=12, filter_size=12, use_cudnn=False)
static_rlt = self.get_static_graph_result(
feed={'pixel': input_array}, fetch_list=[out])[0]
with self.static_graph():
img = layers.data(name='pixel', shape=[3, 2, 2, 2], dtype='float32')
conv3d_transpose = nn.Conv3DTranspose(
'Conv3DTranspose',
num_filters=12,
filter_size=12,
use_cudnn=False)
out = conv3d_transpose(img)
static_rlt2 = self.get_static_graph_result(
feed={'pixel': input_array}, fetch_list=[out])[0]
with self.dynamic_graph():
conv3d_transpose = nn.Conv3DTranspose(
'Conv3DTranspose',
num_filters=12,
filter_size=12,
use_cudnn=False)
dy_rlt = conv3d_transpose(base.to_variable(input_array))
self.assertTrue(np.allclose(static_rlt2, static_rlt))
self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
class TestBook(unittest.TestCase):
def test_fit_a_line(self):
......
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